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  • 1.
    Han, Mengjie
    et al.
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    Canli, Ilkim
    Department of Architecture, Middle East Technical University, Ankara 06800, Türkiye;Center for Solar Energy Research and Applications (ODTÜ-GÜNAM), Middle East Technical University, Ankara 06800, Türkiye.
    Shah, Juveria
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    Zhang, Xingxing
    Högskolan Dalarna, Institutionen för information och teknik, Energiteknik.
    Dino, Ipek Gursel
    Department of Architecture, Middle East Technical University, Ankara 06800, Türkiye;METU Robotics and AI Technologies Application and Research Center (METU-ROMER), Middle East Technical University (METU), Ankara 06800, Türkiye.
    Kalkan, Sinan
    METU Robotics and AI Technologies Application and Research Center (METU-ROMER), Middle East Technical University (METU), Ankara 06800, Türkiye;Department of Computer Engineering, Middle East Technical University, Ankara 06800, Türkiye.
    Perspectives of Machine Learning and Natural Language Processing on Characterizing Positive Energy Districts2024Ingår i: Buildings, E-ISSN 2075-5309, Vol. 14, nr 2, artikel-id 371Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The concept of a Positive Energy District (PED) has become a vital component of the efforts to accelerate the transition to zero carbon emissions and climate-neutral living environments. Research is shifting its focus from energy-efficient single buildings to districts, where the aim is to achieve a positive energy balance across a given time period. Various innovation projects, programs, and activities have produced abundant insights into how to implement and operate PEDs. However, there is still no agreed way of determining what constitutes a PED for the purpose of identifying and evaluating its various elements. This paper thus sets out to create a process for characterizing PEDs. First, nineteen different elements of a PED were identified. Then, two AI techniques, machine learning (ML) and natural language processing (NLP), were introduced and examined to determine their potential for modeling, extracting, and mapping the elements of a PED. Lastly, state-of-the-art research papers were reviewed to identify any contribution they can make to the determination of the effectiveness of the ML and NLP models. The results suggest that both ML and NLP possess significant potential for modeling most of the identified elements in various areas, such as optimization, control, design, and stakeholder mapping. This potential is realized through the utilization of vast amounts of data, enabling these models to generate accurate and useful insights for PED planning and implementation. Several practical strategies have been identified to enhance the characterization of PEDs. These include a clear definition and quantification of the elements, the utilization of urban-scale energy modeling techniques, and the development of user-friendly interfaces capable of presenting model insights in an accessible manner. Thus, developing a holistic approach that integrates existing and novel techniques for PED characterization is essential to achieve sustainable and resilient urban environments.

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  • 2. Wang, Zhenwu
    et al.
    Liu, Fanghan
    Han, Mengjie
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    Tang, Hongjian
    Wan, Benting
    PML-ED: A method of partial multi-label learning by using encoder-decoder framework and exploring label correlation2024Ingår i: Information Sciences, ISSN 0020-0255, E-ISSN 1872-6291, Vol. 661, artikel-id 120165Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Partial multi-label learning (PML) addresses problems where each instance is assigned a candidate label set and only a subset of these candidate labels is correct. The major challenge of PML is that the training procedure can be easily misguided by noisy labels. Current studies on PML have revealed two significant drawbacks. First, most of them do not sufficiently explore complex label correlations, which could improve the effectiveness of label disambiguation. Second, PML models heavily rely on prior assumptions, limiting their applicability to specific scenarios. In this work, we propose a novel method of PML based on the Encoder-Decoder Framework (PML-ED) to address the drawbacks. PML-ED initially achieves the distribution of label probability through a KNN label attention mechanism. It then adopts Conditional Layer Normalization (CLN) to extract the high-order label correlation and relaxes the prior assumption of label noise by introducing a universal Encoder-Decoder framework. This approach makes PML-ED not only more efficient compared to the state-of-the-art methods, but also capable of handling the data with large noisy labels across different domains. Experimental results on 28 benchmark datasets demonstrate that the proposed PML-ED model, when benchmarked against nine leading-edge PML algorithms, achieves the highest average ranking across five evaluation criteria.

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  • 3. Wang, Zhenwu
    et al.
    Xue, Liang
    Guo, Yinan
    Han, Mengjie
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    Liang, Shangchao
    Solving dynamic multi-objective optimization problems via quantifying intensity of environment changes and ensemble learning-based prediction strategies2024Ingår i: Applied Soft Computing, ISSN 1568-4946, E-ISSN 1872-9681, Vol. 154, artikel-id 111317Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Algorithms designed to solve dynamic multi-objective optimization problems (DMOPs) need to consider all of themultiple conflicting objectives to determine the optimal solutions. However, objective functions, constraints orparameters can change over time, which presents a considerable challenge. Algorithms should be able not only toidentify the optimal solution but also to quickly detect and respond to any changes of environment. In order toenhance the capability of detection and response to environmental changes, we propose a dynamic multiobjectiveoptimization (DMOO) algorithm based on the detection of environment change intensity andensemble learning (DMOO-DECI&EL). First, we propose a method for detecting environmental change intensity,where the change intensity is quantified and used to design response strategies. Second, a series of responsestrategies under the framework of ensemble learning are given to handle complex environmental changes.Finally, a boundary learning method is introduced to enhance the diversity and uniformity of the solutions.Experimental results on 14 benchmark functions demonstrate that the proposed DMOO-DECI&EL algorithmachieves the best comprehensive performance across three evaluation criteria, which indicates that DMOODECI&EL has better robustness and convergence and can generate solutions with better diversity compared tofive other state-of-the-art dynamic prediction strategies. In addition, the application of DMOO-DECI&EL to thereal-world scenario, namely the economic power dispatch problem, shows that the proposed method caneffectively handle real-world DMOPs.

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  • 4.
    Sadeghian, Paria
    et al.
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    Han, Mengjie
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    Håkansson, Johan
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    Zhao, Xiaoyun
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    Testing feasibility of using a hidden Markov model on predicting human mobility based on GPS tracking data2024Ingår i: Transportmetrica B: Transport Dynamics, ISSN 2168-0566, Vol. 12, nr 1, artikel-id 2336037Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Human mobility behaviour is far from random and can be predictable. Predicting human mobility behaviour has the potential to improve location selection for facilities, transportation services, urban planning, and can be beneficial in providing more efficient sustainable urban development strategies. However, it is difficult to model urban mobility patterns since incentives for mobility is complex, and influenced by several factors, such as dynamic population, weather conditions. Thus, this paper proposes a prediction-oriented algorithm under the framework of a Hidden Markov Model to predict next-location and time-of-arrival of human mobility. A comprehensive evaluation of these two schemes for the representation of latent and observable variables is discussed. In conclusion, the paper provides a valuable contribution to the field of mobility behaviour prediction by proposing a novel algorithm. The evaluation shows that the proposed algorithm is stable and consistent in predicting the next location of users based on their past trajectories. © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

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  • 5. Wang, Zhenwu
    et al.
    Zhang, Wenteng
    Guo, Yinan
    Han, Mengjie
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    Wan, Benting
    Liang, Shangchao
    A multi-objective chicken swarm optimization algorithm based on dual external archive with various elites2023Ingår i: Applied Soft Computing, ISSN 1568-4946, E-ISSN 1872-9681, Vol. 133, artikel-id 109920Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Multi-objective optimization problems (MOPs) that widely exist in real world concern all optimal solutions compromised among multiple objectives. Chicken swarm optimization algorithm derived from emergent behaviors of organisms provides an effective way for handling MOPs. To speed up convergence and improve uniformity of Pareto-optimal solutions, a multi-objective chicken swarm optimization algorithm based on dual external archives and boundary learning strategy (MOCSO-DABL) is proposed in this paper. Dual external archives are employed to distinguish and choose two types of elite solutions, with the purpose of more effectively guiding individual evolution. A boundary learning strategy guides the chickens to learn from boundary individuals in the later stage of evolution. Moreover, fast non-dominated sorting is adopted to establish the hierarchical social structure of a chicken population, and learning strategies of roosters, hens and chicks are improved to meet the requirements of MOPs. Experimental results on 14 benchmark functions show that the proposed MOCSO-DABL outperforms other five state-of-the-art algorithms significantly.

  • 6. Wan, Benting
    et al.
    Hu, Zhaopeng
    Garg, Harish
    Cheng, Youyu
    Han, Mengjie
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    An integrated group decision-making method for the evaluation of hypertension follow-up systems using interval-valued q-rung orthopair fuzzy sets2023Ingår i: Complex & Intelligent Systems, ISSN 2199-4536, E-ISSN 2198-6053, Vol. 9, nr 4, s. 4521-4554Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    It is imperative to comprehensively evaluate the function, cost, performance and other indices when purchasing a hypertension follow-up (HFU) system for community hospitals. To select the best software product from multiple alternatives, in this paper, we develop a novel integrated group decision-making (GDM) method for the quality evaluation of the system under the interval-valued q-rung orthopair fuzzy sets (IVq-ROFSs). The design of our evaluation indices is based on the characteristics of the HFU system, which in turn represents the evaluation requirements of typical software applications and reflects the particularity of the system. A similarity is extended to measure the IVq-ROFNs, and a new score function is devised for distinguishing IVq-ROFNs to figure out the best IVq-ROFN. The weighted fairly aggregation (WFA) operator is then extended to the interval-valued q-rung orthopair WFA weighted average operator (IVq-ROFWFAWA) for aggregating information. The attribute weights are derived using the LINMAP model based on the similarity of IVq-ROFNs. We design a new expert weight deriving strategy, which makes each alternative have its own expert weight, and use the ARAS method to select the best alternative based on these weights. With these actions, a GDM algorithm that integrates the similarity, score function, IVq-ROFWFAWA operator, attribute weights, expert weights and ARAS is proposed. The applicability of the proposed method is demonstrated through a case study. Its effectiveness and feasibility are verified by comparing it to other state-of-the-art methods and operators.

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  • 7.
    Zhang, Xingxing
    et al.
    Högskolan Dalarna, Institutionen för information och teknik, Energiteknik.
    Shah, Juveria
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    Han, Mengjie
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    ChatGPT for Fast Learning of Positive Energy District (PED): A Trial Testing and Comparison with Expert Discussion Results2023Ingår i: Buildings, E-ISSN 2075-5309, Vol. 13, nr 6, artikel-id 1392Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Positive energy districts (PEDs) are urban areas which seek to take an integral approach to climate neutrality by including technological, spatial, regulatory, financial, legal, social, and economic perspectives. It is still a new concept and approach for many stakeholders. ChatGPT, a generative pre-trained transformer, is an advanced artificial intelligence (AI) chatbot based on a complex network structure and trained by the company OpenAI. It has the potential for the fast learning of PED. This paper reports a trial test in which ChatGPT is used to provide written formulations of PEDs within three frameworks: challenge, impact, and communication and dissemination. The results are compared with the formulations derived from over 80 PED experts who took part in a two-day workshop discussing many aspects of PED research and development. The proposed methodology involves querying ChatGPT with specific questions and recording its responses. Subsequently, expert opinions on the same questions are provided to ChatGPT, aiming to elicit a comparison between the two sources of information. This approach enables an evaluation of ChatGPT’s answers in relation to the insights shared by domain experts. By juxtaposing the outputs, a comprehensive assessment can be made regarding the reliability, accuracy, and alignment of ChatGPT’s responses with expert viewpoints. It is found that ChatGPT can be a useful tool for the rapid formulation of basic information about PEDs that could be used for its wider dissemination amongst the general public. The model is also noted as having a number of limitations, such as providing pre-set single answers, a sensitivity to the phrasing of questions, a tendency to repeat non-important (or general) information, and an inability to assess inputs negatively or provide diverse answers to context-based questions. Its answers were not always based on up-to-date information. Other limitations and some of the ethical–social issues related to the use of ChatGPT are also discussed. This study not only validated the possibility of using ChatGPT to rapid study PEDs but also trained ChatGPT by feeding back the experts’ discussion into the tool. It is recommended that ChatGPT can be involved in real-time PED meetings or workshops so that it can be trained both iteratively and dynamically. © 2023 by the authors.

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  • 8.
    Han, Mengjie
    et al.
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    Shah, Juveria
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    Zhang, Xingxing
    Högskolan Dalarna, Institutionen för information och teknik, Energiteknik.
    Review of natural language processing techniques for characterizing positive energy districts2023Ingår i: journal of Physics; Conference series, Institute of Physics Publishing (IOPP), 2023, Vol. 2600, nr 8, artikel-id 082024Konferensbidrag (Refereegranskat)
    Abstract [en]

    The concept of Positive Energy Districts (PEDs) has emerged as a crucial aspect of endeavours aimed at accelerating the transition to zero carbon emissions and climate-neutral living spaces. The focus of research has shifted from energy-efficient individual buildings to entire districts, where the objective is to achieve a positive energy balance over a specific timeframe. The consensus on the conceptualization of a PED has been evolving and a standardized checklist for identifying and evaluating its constituent elements needs to be addressed. This study aims to develop a methodology for characterizing PEDs by leveraging natural language processing (NLP) techniques to model, extract, and map these elements. Furthermore, a review of state-of-the-art research papers is conducted to ascertain their contribution to assessing the effectiveness of NLP models. The findings indicate that NLP holds significant potential in modelling the majority of the identified elements across various domains. To establish a systematic framework for AI modelling, it is crucial to adopt approaches that integrate established and innovative techniques for PED characterization. Such an approach would enable a comprehensive and effective implementation of NLP within the context of PEDs, facilitating the creation of sustainable and resilient urban environments. © 2023 Institute of Physics Publishing. All rights reserved.

  • 9.
    Shah, Juveria
    et al.
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    Saini, Puneet
    Högskolan Dalarna, Institutionen för information och teknik, Energiteknik. Absolicon solar AB, Härnösand; Department of engineering sciences, Uppsala univeristy.
    Han, Mengjie
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    Analysis And Performance Mapping Of “Component To System” For A Parabolic Trough Collector Applied To Process Heating Applications2022Ingår i: ISEC 2022, 2022, s. 487-488Konferensbidrag (Refereegranskat)
    Abstract [en]

    The slogan “Heat is half” is of importance to keep in mind that nearly 50 % of the final energy use is in the form of heat. The global efforts for future decarbonised heating systems are based on hydrogen and electrification of heating etc. Solar thermal technology is a key component of greener industrial heating solutions. Solar thermal technologies for process heating application has decade long history of implementation and are gaining significant interest from all around the world. The performance prediction of solar thermal technologies on the system level is more complicated compared to photovoltaic, due to the effect of performance on system boundary conditions such as variation in meteorological parameters, load demand, temperature levels, thermal storage type. The central focus of this paper is on the use of a parabolic trough collector (PTC) for process heating applications in the medium temperature range. The aim of this paper is to map the performance of PTC collector into an industrial system, and to analyse the decrease in collector thermal output from component level to system level. The simulations are implemented in TRNSYS and MATLAB. The results are visualized using QGIS tool to generate the heat map for performance parameters for a range of solar fractions.

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  • 10.
    Zhao, Jing
    et al.
    School of Business Administration, Xi'an Eurasia University, Yanta District, Xi'an, China.
    Han, Mengjie
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    Wang, Zhenwu
    Department of Computer Science and Technology, China University of Mining and Technology, Beijing, China.
    Wan, Benting
    School of Software and IoT Engineering, Jiangxi University of Finance and Economics, Nanchang, China.
    Autoregressive count data modeling on mobility patterns to predict cases of COVID-19 infection2022Ingår i: Stochastic environmental research and risk assessment (Print), ISSN 1436-3240, E-ISSN 1436-3259, Vol. 36, nr 12, s. 4185-4200Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    At the beginning of 2022 the global daily count of new cases of COVID-19 exceeded 3.2 million, a tripling of the historical peak value reported between the initial outbreak of the pandemic and the end of 2021. Aerosol transmission through interpersonal contact is the main cause of the disease's spread, although control measures have been put in place to reduce contact opportunities. Mobility pattern is a basic mechanism for understanding how people gather at a location and how long they stay there. Due to the inherent dependencies in disease transmission, models for associating mobility data with confirmed cases need to be individually designed for different regions and time periods. In this paper, we propose an autoregressive count data model under the framework of a generalized linear model to illustrate a process of model specification and selection. By evaluating a 14-day-ahead prediction from Sweden, the results showed that for a dense population region, using mobility data with a lag of 8 days is the most reliable way of predicting the number of confirmed cases in relative numbers at a high coverage rate. It is sufficient for both of the autoregressive terms, studied variable and conditional expectation, to take one day back. For sparsely populated regions, a lag of 10 days produced the lowest error in absolute value for the predictions, where weekly periodicity on the studied variable is recommended for use. Interventions were further included to identify the most relevant mobility categories. Statistical features were also presented to verify the model assumptions.

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  • 11.
    Huang, Pei
    et al.
    Högskolan Dalarna, Institutionen för information och teknik, Energiteknik.
    Han, Mengjie
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    Zhang, Xingxing
    Högskolan Dalarna, Institutionen för information och teknik, Energiteknik.
    Hussain, Sayed Asad
    The University of British Columbia, Canada.
    Jayprakash Bhagat, Rohit
    Hogarehalli Kumar, Deepu
    Characterization and optimization of energy sharing performances in energy-sharing communities in Sweden, Canada and Germany2022Ingår i: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 326, artikel-id 120044Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Peer-to-peer (P2P) renewable power sharing within a building community is a promising solution to enhance the community's self-sufficiency and relieve the grid stress posed by the increased deployment of distributed renewable power. Existing studies have pointed out that the energy sharing potentials of a building community are affected by various factors including location, community scale, renewable energy system (RES) capacity, energy system type, storage integration, etc. However, the impacts of these factors on the energy sharing potentials in a building community are not fully studied. Being unaware of those factors’ impacts could lead to reduced energy sharing potentials and thus limit the associated improvement in energy and economic performances. Thus, this study conducts a comprehensive analysis of various factors’ impacts on the energy sharing performances in building communities. Two performance indicators are first proposed to quantify the energy sharing performances: total amount of energy sharing and energy sharing ratio (ESR). Then, parametric studies are conducted based on real electricity demand data in three countries to reveal how these factors affect the proposed indictors and improvements in self-sufficiency, electricity costs, and energy exchanges with the power grid. Next, a genetic algorithm based design method is developed to optimize the influential parameters to maximize the energy sharing potentials in a community. The study results show that the main influential factors are RES capacity ratio, PV capacity ratio, and energy storage system capacity. A large energy storage capacity can enhance the ESR. To achieve the maximized ESR, the optimal RES capacity ratio should be around 0.4 ∼ 1.1. The maximum energy sharing ratio is usually smaller in high latitude districts such as Sweden. This study characterizes the energy sharing performances and provides a novel perspective to optimize the design of energy systems in energy sharing communities. It can pave the way for the large integration of distributed renewable power in the future. © 2022 The Author(s)

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  • 12.
    Han, Mengjie
    et al.
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    Johari, Fatemeh
    Uppsala University.
    Huang, Pei
    Högskolan Dalarna, Institutionen för information och teknik, Energiteknik.
    Zhang, Xingxing
    Högskolan Dalarna, Institutionen för information och teknik, Energiteknik.
    Generating hourly electricity demand data for large-scale single-family buildings by a decomposition-recombination method2022Ingår i: Energy and Built Environment, ISSN 2666-1233Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Household electricity demand has substantial impacts on local grid operation, energy storage and the energy performance of buildings. Hourly demand data at district or urban level helps stakeholders understand the demand patterns from a granular time scale and provides robust evidence in energy management. However, such type of data is often expensive and time-consuming to collect, process and integrate. Decisions built upon smart meter data have to deal with challenges of privacy and security in the whole process. Incomplete data due to confidentiality concerns or system failure can further increase the difficulty of modeling and optimization. In addition, methods using historical data to make predictions can largely vary depending on data quality, local building environment, and dynamic factors. Considering these challenges, this paper proposes a statistical method to generate hourly electricity demand data for large-scale single-family buildings by decomposing time series data and recombining them into synthetics. The proposed method used public data to capture seasonality and the distribution of residuals that fulfill statistical characteristics. A reference building was used to provide empirical parameter settings and validations for the studied buildings. An illustrative case in a city of Sweden using only annual total demand was presented for deploying the proposed method. The results showed that the proposed method can mimic reality well and represent a high level of similarity to the real data. The average monthly error for the best month reached 15.9% and the best one was below 10% among 11 tested months. Less than 0.6% improper synthetic values were found in the studied region.

  • 13.
    Huang, Pei
    et al.
    Högskolan Dalarna, Institutionen för information och teknik, Energiteknik.
    Tu, Ran
    Zhang, Xingxing
    Högskolan Dalarna, Institutionen för information och teknik, Energiteknik.
    Han, Mengjie
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    Sun, Yongjun
    Hussain, Syed Asad
    Zhang, Linfeng
    Investigation of electric vehicle smart charging characteristics on the power regulation performance in solar powered building communities and battery degradation in Sweden2022Ingår i: Journal of Energy Storage, ISSN 2352-152X, E-ISSN 2352-1538, Vol. 56, s. 105907-105907, artikel-id 105907Artikel i tidskrift (Refereegranskat)
  • 14.
    Salin, Hannes
    et al.
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys. Swedish Transport Administration, Borlänge, Sweden.
    Rybarczyk, Yves
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    Han, Mengjie
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    Nyberg, Roger G
    Högskolan Dalarna, Institutionen för information och teknik, Informatik.
    Quality Metrics for Software Development Management and Decision Making: An Analysis of Attitudes and Decisions2022Ingår i: Product-Focused Software Process Improvement. 23rd International Conference, PROFES 2022, Jyväskylä, Finland, November 21–23, 2022, Proceedings / [ed] Taibi, D., Kuhrmann, M., Mikkonen, T., Klünder, J., Abrahamsson, P., Springer, 2022, Vol. 13709, s. 525-530Konferensbidrag (Refereegranskat)
    Abstract [en]

    We combine current literature in software quality metrics with an attitude validation study with industry practitioners, to establish how quality metrics can be used for data-driven approaches. We also propose a simple metric nomenclature and map our findings into a decision making model for easy adoption and utilization of data-driven decision-making methods. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

  • 15.
    Wan, Benting
    et al.
    Jiangxi Univ Finance & Econ, Sch Software & IoT Engn, Nanchang 330013, Jiangxi, Peoples R China..
    Lu, Ruyi
    Jiangxi Univ Finance & Econ, Sch Software & IoT Engn, Nanchang 330013, Jiangxi, Peoples R China..
    Han, Mengjie
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    Weighted average LINMAP group decision-making method based on q-rung orthopair triangular fuzzy numbers2022Ingår i: Granular Computing, ISSN 2364-4966, Vol. 7, nr 3, s. 489-503Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Considering the situation where decision values are q-rung orthopair triangular fuzzy number (q-ROTFN) and pair-wise comparisons of alternatives and evaluation matrices are given by decision-makers, a new group decision-making method is necessary to be studied for solving a group decision-making problem in the above situation. In this paper, we firstly proposed a q-rung orthopair triangular fuzzy weighted average (q-ROTFWA) operator based on the WA operator. In a second step, a linear programming technique for the multidimensional analysis of preferences (LINMAP) model based on q-ROTFN was formulated, which is used to obtain the weight of each attribute through partial preference information. A distance formula was introduced to get the ranking order of schemes and the best alternative. Finally, the weighted average LINMAP (WA-LINMAP) method was illustrated in a case study to verify its effectiveness. It is found in the experiment that the change of the q value does not affect the ranking of the schemes. The comparative analysis further confirms the effectiveness and feasibility of the proposed method.

  • 16. Jin, Yuan
    et al.
    Yan, Da
    Zhang, Xingxing
    Högskolan Dalarna, Institutionen för information och teknik, Energiteknik.
    An, Jingjing
    Han, Mengjie
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    A data-driven model predictive control for lighting system based on historical occupancy in an office building: Methodology development2021Ingår i: Building Simulation, ISSN 1996-3599, E-ISSN 1996-8744, Vol. 14, s. 219-235Artikel i tidskrift (Refereegranskat)
  • 17.
    Quintana, Samer
    et al.
    Högskolan Dalarna, Institutionen för information och teknik, Energiteknik.
    Huang, Pei
    Högskolan Dalarna, Institutionen för information och teknik, Energiteknik.
    Han, Mengjie
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    Zhang, Xingxing
    Högskolan Dalarna, Institutionen för information och teknik, Energiteknik.
    A Top‐Down Digital Mapping of Spatial‐Temporal Energy Use for Municipality‐Owned Buildings: A Case Study in Borlänge, Sweden2021Ingår i: Buildings, E-ISSN 2075-5309, Vol. 11, nr 2, artikel-id 72Artikel i tidskrift (Refereegranskat)
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  • 18.
    Han, Mengjie
    et al.
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    Wang, Zhenwu
    Zhang, Xingxing
    Högskolan Dalarna, Institutionen för information och teknik, Energiteknik.
    An Approach to Data Acquisition for Urban Building Energy Modeling Using a Gaussian Mixture Model and Expectation-Maximization Algorithm2021Ingår i: Buildings, E-ISSN 2075-5309, Vol. 11, nr 1Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In recent years, a building’s energy performance is becoming uncertain because of factors such as climate change, the Covid-19 pandemic, stochastic occupant behavior and inefficient building control systems. Sufficient measurement data is essential to predict and manage a building’s performance levels. Assessing energy performance of buildings at an urban scale requires even larger data samples in order to perform an accurate analysis at an aggregated level. However, data are not only expensive, but it can also be a real challenge for communities to acquire large amounts of real energy data. This is despite the fact that inadequate knowledge of a full population will lead to biased learning and the failure to establish a data pipeline. Thus, this paper proposes a Gaussian mixture model (GMM) with an Expectation-Maximization (EM) algorithm that will produce synthetic building energy data. This method is tested on real datasets. The results show that the parameter estimates from the model are stable and close to the true values. The bivariate model gives better performance in classification accuracy. Synthetic data points generated by the models show a consistent representation of the real data. The approach developed here can be useful for building simulations and optimizations with spatio-temporal mapping.

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  • 19.
    Zhang, Xingxing
    et al.
    Högskolan Dalarna, Institutionen för information och teknik, Energiteknik.
    Shen, Jingchun
    Högskolan Dalarna, Institutionen för information och teknik, Byggteknik.
    Saini, Puneet
    Högskolan Dalarna, Institutionen för information och teknik, Energiteknik.
    Lovati, Marco
    Högskolan Dalarna, Institutionen för information och teknik, Energiteknik.
    Han, Mengjie
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    Huang, Pei
    Högskolan Dalarna, Institutionen för information och teknik, Energiteknik.
    Huang, Zhihua
    Telenor Connexion AB, Stockholm.
    Digital Twin for Accelerating Sustainability in Positive Energy District: A Review of Simulation Tools and Applications2021Ingår i: Frontiers in Sustainable Cities, E-ISSN 2624-9634, Vol. 3, artikel-id 663269Artikel, forskningsöversikt (Refereegranskat)
    Abstract [en]

    A digital twin is regarded as a potential solution to optimize positive energy districts (PED). This paper presents a compact review about digital twins for PED from aspects of concepts, working principles, tools/platforms, and applications, in order to address the issues of both how a digital PED twin is made and what tools can be used for a digital PED twin. Four key components of digital PED twin are identified, i.e., a virtual model, sensor network integration, data analytics, and a stakeholder layer. Very few available tools now have full functions for digital PED twin, while most tools either have a focus on industrial applications or are designed for data collection, communication and visualization based on building information models (BIM) or geographical information system (GIS). Several observations gained from successful application are that current digital PED twins can be categorized into three tiers: (1) an enhanced version of BIM model only, (2) semantic platforms for data flow, and (3) big data analysis and feedback operation. Further challenges and opportunities are found in areas of data analysis and semantic interoperability, business models, data security, and management. The outcome of the review is expected to provide useful information for further development of digital PED twins and optimizing its sustainability.

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  • 20. Dong, Bing
    et al.
    Liu, Yapan
    Fontenot, Hannah
    Ouf, Mohamed
    Osman, Mohamed
    Chong, Adrian
    Qin, Shuxu
    Han, Mengjie
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    Zhang, Xingxing
    Högskolan Dalarna, Institutionen för information och teknik, Energiteknik.
    Carlucci, Salvatore
    Occupant behavior modeling methods for resilient building design,operation and policy at urban scale: a review2021Ingår i: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 293, artikel-id 116856Artikel i tidskrift (Refereegranskat)
  • 21.
    Han, Mengjie
    et al.
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    May, Ross
    Zhang, Xingxing
    Högskolan Dalarna, Institutionen för information och teknik, Energiteknik.
    Reinforcement Learning Methodologies for Controlling Occupant Comfort in Buildings2021Ingår i: Data-driven Analytics for Sustainable Buildings and Cities, Switzerland: Springer, 2021, s. 179-205Kapitel i bok, del av antologi (Övrigt vetenskapligt)
  • 22.
    Han, Mengjie
    et al.
    Högskolan Dalarna, Institutionen för information och teknik, Mikrodataanalys.
    Zhao, Jing
    Zhang, Xingxing
    Högskolan Dalarna, Institutionen för information och teknik, Energiteknik.
    Shen, Jingchun
    Högskolan Dalarna, Institutionen för information och teknik, Byggteknik.
    Li, Yu
    The reinforcement learning method for occupant behavior in building control: A review2021Ingår i: Energy and Built Environment, ISSN 2666-1233, Vol. 2, nr 2, s. 137-148Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Occupant behavior in buildings has been considered the major source of uncertainty for assessing energy consumption and building performance. Modeling frameworks are usually built to accomplish a certain task, but the stochasticity of the occupant makes it difficult to apply that experience to a similar but distinct environment. For complex and dynamic environments, the development of smart devices and computing power makes intelligent control methods for occupant behaviors more viable. It is expected that they will make a substantial contribution to reducing global energy consumption. Among these control techniques, the reinforcement learning (RL) method seems distinctive and applicable. The success of the reinforcement learning method in many artificial intelligence applications has given an explicit indication of how this method might be used to model and adjust occupant behavior in building control. Fruitful algorithms complement each other and guarantee the quality of the optimization. However, the examination of occupant behavior based on reinforcement learning methodologies is not well established. The way that occupant interacts with the RL agent is still unclear. This study briefly reviews the empirical applications using reinforcement learning, how they have contributed to shaping the modeling paradigms and how they might suggest a future research direction.

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  • 23.
    Han, Mengjie
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    May, Ross
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Zhang, Xingxing
    Högskolan Dalarna, Akademin Industri och samhälle, Energiteknik.
    Wang, Xinru
    Pan, Song
    Da, Yan
    Jin, Yuan
    A novel reinforcement learning method for improving occupant comfort via window opening and closing2020Ingår i: Sustainable cities and society, ISSN 2210-6707, Vol. 61, artikel-id 102247Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    An occupant's window opening and closing behaviour can significantly influence the level of comfort in the indoor environment. Such behaviour is, however, complex to predict and control conventionally. This paper, therefore, proposes a novel reinforcement learning (RL) method for the advanced control of window opening and closing. The RL control aims at optimising the time point for window opening/closing through observing and learning from the environment. The theory of model-free RL control is developed with the objective of improving occupant comfort, which is applied to historical field measurement data taken from an office building in Beijing. Preliminary testing of RL control is conducted by evaluating the control method’s actions. The results show that the RL control strategy improves thermal and indoor air quality by more than 90 % when compared with the actual historically observed occupant data. This methodology establishes a prototype for optimally controlling window opening and closing behaviour. It can be further extended by including more environmental parameters and more objectives such as energy consumption. The model-free characteristic of RL avoids the disadvantage of implementing inaccurate or complex models for the environment, thereby enabling a great potential in the application of intelligent control for buildings.

  • 24. Wang, Zhenwu
    et al.
    Wan, Benting
    Han, Mengjie
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    A Three-Dimensional Visualization Framework forUnderground Geohazard Recognition on UrbanRoad-Facing GPR Data2020Ingår i: ISPRS International Journal of Geo-Information, ISSN 2220-9964, Vol. 9, nr 11, artikel-id 668Artikel i tidskrift (Refereegranskat)
  • 25. Li, Y
    et al.
    Rezgui, Y
    Guerriero, A
    Zhang, Xingxing
    Högskolan Dalarna, Akademin Industri och samhälle, Energiteknik.
    Han, Mengjie
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Kubicki, S
    Yan, D
    Development of an adaptation table to enhance the accuracy of the predicted mean vote model2020Ingår i: Building and Environment, ISSN 0360-1323, E-ISSN 1873-684X, Vol. 168, artikel-id 106504Artikel i tidskrift (Refereegranskat)
  • 26. Carlucci, S.
    et al.
    De Simone, M.
    Firth, S. K.
    Kjærgaard, M. B.
    Markovic, R.
    Rahaman, M. S.
    Annaqeeb, M. K.
    Biandrate, S.
    Han, Mengjie
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    van Treeck, C.
    Modeling occupant behavior in buildings2020Ingår i: Building and Environment, ISSN 0360-1323, E-ISSN 1873-684X, Vol. 174, artikel-id 106768Artikel i tidskrift (Refereegranskat)
  • 27. Zhenwu, Wang
    et al.
    Tielin, Wang
    Benting, Wan
    Han, Mengjie
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Partial Classifier Chains with Feature Selection by Exploiting Label Correlation in Multi-Label Classification2020Ingår i: Entropy, E-ISSN 1099-4300, Vol. 22, nr 1143, s. 1-22Artikel i tidskrift (Refereegranskat)
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  • 28.
    Han, Mengjie
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    May, Ross
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Zhang, Xingxing
    Högskolan Dalarna, Akademin Industri och samhälle, Energiteknik.
    Wang, Xinru
    Pan, Song
    Yan, Da
    Jin, Yuan
    Xu, Liguo
    A review of reinforcement learning methodologies for controlling occupant comfort in buildings2019Ingår i: Sustainable cities and society, ISSN 2210-6707, Vol. 51, artikel-id 101748Artikel i tidskrift (Refereegranskat)
  • 29.
    Zhang, Xingxing
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Energiteknik.
    Wu, J.
    Pan, S.
    Han, Mengjie
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    An economic analysis of the solar photovoltaic/thermal (PV/T) technologies in Sweden: A case study2019Ingår i: IOP Conference Series: Materials Science and Engineering, 2019, Vol. 556, nr 1, artikel-id 012002Konferensbidrag (Refereegranskat)
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  • 30. Jin, Y.
    et al.
    Yan, D.
    Zhang, Xingxing
    Högskolan Dalarna, Akademin Industri och samhälle, Energiteknik.
    Han, Mengjie
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Kang, X.
    An, J.
    Sun, H.
    District household electricity consumption pattern analysis based on auto-encoder algorithm2019Ingår i: IOP Conference Series: Materials Science and Engineering, 2019, Vol. 609, nr 7, artikel-id 072028Konferensbidrag (Refereegranskat)
    Abstract [en]

    The energy shortage is one key issue for sustainable development, a potential solution of which is the integration with the renewable energy resources. However, the temporal sequential characteristic of renewable resources is different from traditional power grid. For the entire power grid, it is essential to match the energy generation side with the energy consumption side, so the load characteristic at the energy use side is crucial for renewable power integration. Better understanding of energy consumption pattern in buildings contributes to matching different source of energy generation. Under the background of integration of traditional and renewable energy, this research focuses on analysis of different household electricity consumption patterns in an urban scale. The original data is from measurement of daily energy consumption with smart meter in households. To avoid the dimension explosion phenomenon, the auto-encoder algorithm is introduced during the clustering analysis of daily electricity use data, which plays the role of principal component analysis. The clustering based on auto-encoder gives a clear insight into the urban electricity use patterns in household. During the data analysis, several feature variables are proposed, which include peak value, valley value and average value. The distinction analysis is also conducted to evaluate the analysis performance. The study takes households in Nanjing city, China as a case study, to conduct the clustering analysis on electricity consumption of residential buildings. The analysis results can be further applied, such as during the capacity design of district energy storage.

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  • 31.
    Han, Mengjie
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Carling, Kenneth
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    GRASP and Statistical Bounds for Heuristic Solutions to Combinatorial Problems2019Ingår i: International Journal of Management and Applied Science, ISSN 2394-7926, Vol. 5, nr 8, s. 113-119Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The quality of a heuristic solution to a NP-hard combinatorial problem is hard to assess. A few studies have advocated and tested statistical bounds as a method for assessment. These studies indicate that statistical bounds are superior to the more widely known and used deterministic bounds. However, the previous studies have been limited to a few heuristics and combinatorial problems and, hence, the general performance of statistical bounds in combinatorial optimization remains an open question. This work complements the existing literature on statistical bounds by testing them on the metaheuristic Greedy Randomized Adaptive Search Procedures (GRASP) and four combinatorial problems. Our findings confirm previous results that statistical bounds are reliable for the p-median problem, while we note that they also seem reliable for the set covering problem. For the quadratic assignment problem, the statistical bounds have previously been found reliable when obtained from the Genetic algorithm whereas in this work they have been found less reliable. Finally, we provide statistical bounds to four 2-path network design problem instances for which the optimum is currently unknown.

  • 32.
    Han, Mengjie
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Håkansson, Johan
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Lundmark, M.
    Intra-urban location of stores and labour turnover in retail2019Ingår i: International Review of Retail Distribution & Consumer Research, ISSN 0959-3969, E-ISSN 1466-4402, Vol. 29, nr 4, s. 359-375Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The aim of this paper is to analyse labour turnover in retail firms with stores in different city locations. This case study of a Swedish mid-sized city uses comprehensive longitudinal register data on individuals. In a first step, an unconditional descriptive analysis shows that labour turnover in retail is higher in out-of-town locations, compared to more central locations in the city. In a second step, a generalized linear model (GLM) analysis is conducted where labour turnover in downtown and out-of-town locations are compared. Firm internal and industry factors, as well as employee characteristics, and location-specific factors are controlled for. The results indicate that commuting costs and intra-urban location have no statistically significant effect on labour turnover in retail firms. Instead, firm internal factors, such as human resource management, has a major influence on labour turnover rates. The findings indicate that in particular firms with multiple locations may need to pay extra attention to work conditions across stores in different places in a city, in order to avoid diverging levels of labour mobility. This paper complements previous survey-based studies on labour turnover by using a comprehensive micro-level dataset to analyse revealed rather than stated preferences concerning job-to-job mobility. An elaborated measure of labour turnover is used to analyse differences between shopping areas in different locations within the city. The particular research design used in this paper makes it possible to isolate the effect of intra-organizational conditions by analysing mobility within firms with workplaces in both downtown and out-of-town locations. This is the first comprehensive study of labour turnover and mobility with an intra-urban perspective in the retail sector.

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  • 33. Wei, Yixuan
    et al.
    Xia, Liang
    Pan, Song
    Wu, Jinshun
    Zhang, Xingxing
    Högskolan Dalarna, Akademin Industri och samhälle, Energiteknik.
    Han, Mengjie
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Zhang, Weiya
    Xie, Jingchao
    Li, Qingping
    Prediction of occupancy level and energy consumption in office building using blind system identification and neural networks2019Ingår i: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 240, s. 276-294Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Occupancy behaviour plays an important role in energy consumption in buildings. Currently, the shallow understanding of occupancy has led to a considerable performance gap between predicted and measured energy use. This paper presents an approach to estimate the occupancy based on blind system identification (BSI), and a prediction model of electricity consumption by an air-conditioning system is developed and reported based on an artificial neural network with the BSI estimation of the number of occupants as an input. This starts from the identification of indoor CO2 dynamics derived from the mass-conservation law and venting levels. The unknown parameters, including the occupancy and model parameters, are estimated by using a frequentist maximum-likelihood algorithm and Bayesian estimation. The second phase is to establish the prediction model of the electricity consumption of the air-conditioning system by using a feed-forward neural network (FFNN) and extreme learning machine (ELM), as well as ensemble models. To analyse some aspects of the benchmark test for identifying the effect of structure parameters and input-selection alternatives, three studies are conducted on (1) the effect of predictor selection based on principal component analysis, (2) the effect of the estimated occupancy as the supplementary input, and (3) the effect of the neural network ensemble. The result shows that the occupancy number, as the input, is able to improve the accuracy in predicting energy consumption using a neural network model.

  • 34.
    May, Ross
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Zhang, Xingxing
    Högskolan Dalarna, Akademin Industri och samhälle, Energiteknik.
    Wu, J.
    Han, Mengjie
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Reinforcement learning control for indoor comfort: A survey2019Ingår i: IOP Conference Series: Materials Science and Engineering, 2019, Vol. 609, nr 6, artikel-id 062011Konferensbidrag (Refereegranskat)
    Abstract [en]

    Building control systems are prone to fail in complex and dynamic environments. The reinforcement learning (RL) method is becoming more and more attractive in automatic control. The success of the reinforcement learning method in many artificial intelligence applications has resulted in an open question on how to implement the method in building control systems. This paper therefore conducts a comprehensive review of the RL methods applied in control systems for indoor comfort and environment. The empirical applications of RL-based control systems are then presented, depending on optimisation objectives and the measurement of energy use. This paper illustrates the class of algorithms and implementation details regarding how the value functions have been represented and how the policies are improved. This paper is expected to clarify the feasible theory and functions of RL for building control systems, which would promote their wider-spread application and thus contribute to the social economic benefits in the energy and built environments.

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  • 35. Wei, Yixuan
    et al.
    Zhang, Xingxing
    Högskolan Dalarna, Akademin Industri och samhälle, Energiteknik.
    Shi, Yong
    Xia, Liang
    Pan, Song
    Wu, Jinshun
    Han, Mengjie
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Zhao, Xiaoyun
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    A review of data-driven approaches for prediction and classification of building energy consumption2018Ingår i: Renewable & sustainable energy reviews, ISSN 1364-0321, E-ISSN 1879-0690, Vol. 82, nr 1, s. 1027-1047Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    A recent surge of interest in building energy consumption has generated a tremendous amount of energy data, which boosts the data-driven algorithms for broad application throughout the building industry. This article reviews the prevailing data-driven approaches used in building energy analysis under different archetypes and granularities, including those methods for prediction (artificial neural networks, support vector machines, statistical regression, decision tree and genetic algorithm) and those methods for classification (K-mean clustering, self-organizing map and hierarchy clustering). The review results demonstrate that the data-driven approaches have well addressed a large variety of building energy related applications, such as load forecasting and prediction, energy pattern profiling, regional energy-consumption mapping, benchmarking for building stocks, global retrofit strategies and guideline making etc. Significantly, this review refines a few key tasks for modification of the data-driven approaches in the context of application to building energy analysis. The conclusions drawn in this review could facilitate future micro-scale changes of energy use for a particular building through the appropriate retrofit and the inclusion of renewable energy technologies. It also paves an avenue to explore potential in macro-scale energy-reduction with consideration of customer demands. All these will be useful to establish a better long-term strategy for urban sustainability.

  • 36.
    Han, Mengjie
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Zhang, Xingxing
    Högskolan Dalarna, Akademin Industri och samhälle, Energiteknik.
    Xu, Liguo
    May, Ross
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Pan, Song
    Wu, Jinshun
    A review of reinforcement learning methodologies on control systems for building energy2018Rapport (Övrigt vetenskapligt)
    Abstract [en]

    The usage of energy directly leads to a great amount of consumption of the non-renewable fossil resources. Exploiting fossil resources energy can influence both climate and health via ineluctable emissions. Raising awareness, choosing alternative energy and developing energy efficient equipment contributes to reducing the demand for fossil resources energy, but the implementation of them usually takes a long time. Since building energy amounts to around one-third of global energy consumption, and systems in buildings, e.g. HVAC, can be intervened by individual building management, advanced and reliable control techniques for buildings are expected to have a substantial contribution to reducing global energy consumptions. Among those control techniques, the model-free, data-driven reinforcement learning method seems distinctive and applicable. The success of the reinforcement learning method in many artificial intelligence applications has brought us an explicit indication of implementing the method on building energy control. Fruitful algorithms complement each other and guarantee the quality of the optimisation. As a central brain of smart building automation systems, the control technique directly affects the performance of buildings. However, the examination of previous works based on reinforcement learning methodologies are not available and, moreover, how the algorithms can be developed is still vague. Therefore, this paper briefly analyses the empirical applications from the methodology point of view and proposes the future research direction.

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  • 37.
    Zhang, Xingxing
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Energiteknik.
    Lovati, Marco
    Vigna, Ilaria
    Widén, Joakim
    Han, Mengjie
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Gál, Csilla V
    Högskolan Dalarna, Akademin Industri och samhälle, Energiteknik.
    Feng, Tao
    A review of urban energy systems at building cluster level incorporating renewable-energy-source (RES) envelope solutions2018Ingår i: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 230, s. 1034-1056Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The emergence of renewable-energy-source (RES) envelope solutions, building retrofit requirements and advanced energy technologies brought about challenges to the existing paradigm of urban energy systems. It is envisioned that the building cluster approach—that can maximize the synergies of RES harvesting, building performance, and distributed energy management—will deliver the breakthrough to these challenges. Thus, this paper aims to critically review urban energy systems at the cluster level that incorporate building integrated RES solutions. We begin with defining cluster approach and the associated boundaries. Several factors influencing energy planning at cluster scale are identified, while the most important ones are discussed in detail. The closely reviewed factors include RES envelope solutions, solar energy potential, density of buildings, energy demand, integrated cluster-scale energy systems and energy hub. The examined categories of RES envelope solutions are (i) the solar power, (ii) the solar thermal and (iii) the energy-efficient ones, out of which solar energy is the most prevalent RES. As a result, methods assessing the solar energy potentials of building envelopes are reviewed in detail. Building density and the associated energy use are also identified as key factors since they affect the type and the energy harvesting potentials of RES envelopes. Modelling techniques for building energy demand at cluster level and their coupling with complex integrated energy systems or an energy hub are reviewed in a comprehensive way. In addition, the paper discusses control and operational methods as well as related optimization algorithms for the energy hub concept. Based on the findings of the review, we put forward a matrix of recommendations for cluster-level energy system simulations aiming to maximize the direct and indirect benefits of RES envelope solutions. By reviewing key factors and modelling approaches for characterizing RES-envelope-solutions-based urban energy systems at cluster level, this paper hopes to foster the transition towards more sustainable urban energy systems.

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  • 38. Pan, S
    et al.
    Xiong, Y
    Han, Y
    Zhang, Xingxing
    Högskolan Dalarna, Akademin Industri och samhälle, Energiteknik.
    Xia, L
    Wei, S
    Wu, J
    Han, Mengjie
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    A study on influential factors of occupant window-opening behavior in an office building in China2018Ingår i: Building and Environment, ISSN 0360-1323, E-ISSN 1873-684X, Vol. 133, s. 41-50Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Occupants often perform many types of behavior in buildings to adjust the indoor thermal environment. In these types, opening/closing the windows, often regarded as window-opening behavior, is more commonly observed because of its convenience. It not only improves indoor air quality to satisfy occupants' requirement for indoor thermal comfort but also influences building energy consumption. To learn more about potential factors having effects on occupants' window-opening behavior, a field study was carried out in an office building within a university in Beijing. Window state (open/closed) for a total of 5 windows in 5 offices on the second floor in 285 days (9.5 months) were recorded daily. Potential factors, categorized as environmental and non-environmental ones, were subsequently identified with their impact on window-opening behavior through logistic regression and Pearson correlation approaches. The analytical results show that occupants' window-opening behavior is more strongly correlated to environmental factors, such as indoor and outdoor air temperatures, wind speed, relative humidity, outdoor FM2.5 concentrations, solar radiation, sunshine hours, in which air temperatures dominate the influence. While the non-environmental factors, i.e. seasonal change, time of day and personal preference, also affects the patterns of window-opening probability. This paper provides solid field data on occupant window opening behavior in China, with high resolutions and demonstrates the way in analyzing and predicting the probability of window-opening behavior. Its discussion into the potential impact factors shall be useful for further investigation of the relationship between building energy consumption and window-opening behavior.

  • 39.
    Han, Mengjie
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Mihaescu, Oana
    HUI Research, Sweden.
    Li, Yujiao
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Rudholm, Niklas
    Högskolan Dalarna, Akademin Industri och samhälle, Nationalekonomi. HUI Research, Sweden.
    Comparison and one-stop shopping after big-box retail entry: a spatial difference-in-difference analysis2018Ingår i: Journal of Retailing and Consumer Services, ISSN 0969-6989, E-ISSN 1873-1384, Vol. 40, s. 175-187Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This paper empirically measures the potential spillover effects of big-box retail entry on the productivity of incumbent retailers in the entry regions, and investigates whether the effects differ depending on 1) if the entry is in a rural or urban area, and 2) if the incumbent retailers are within retail industries selling substitute or complement goods to those found in IKEA. To identify the IKEA-entry effect, a difference-in-difference model is suitable, but traditionally such estimators neglect the possibility that firms’ sales are determined by a process with spatially interactive responses. If ignored, these responses may cause biased estimates of the IKEA entry effect due to spatial heterogeneity of the treatment effect. One objective of this paper is thus to propose a spatial difference-in-difference estimator accounting for possible spatial spillover effects of IKEA entry. Particular emphasis is placed on the development of a suitable weight matrix accounting for the spatial links between firms, where we allow for local spatial interactions such that the outcome of observed units depends both on their own treatment as well as on the treatment of their neighbors. Our results show that for complementary goods retailers (or one-stop shopping retailers) in Haparanda and Kalmar, productivity increased by 35% and 18%, respectively, due to IKEA entry. No statistically significant effects were found for the entries in Karlstad and Gothenburg, indicating that it is mainly incumbents in smaller entry regions that benefit from IKEA entry. Also, for incumbent retailers selling substitute (or comparison shopping) goods no significant effects were found in any of the entry regions, indicating that it is mainly retailers selling complementary goods that benefit from IKEA entry. Finally, our results also show that ignoring the possibility of spatially correlated treatment effects in the regression models reduces the estimated impact of the IKEA entries in Haparanda and Kalmar on productivity in one-stop shopping retail firms with 3% and 0.1% points, respectively. © 2017 Elsevier Ltd

  • 40. Gu, Yaxiu
    et al.
    Zhang, Xingxing
    Högskolan Dalarna, Akademin Industri och samhälle, Energiteknik.
    Myhren, Jonn Are
    Högskolan Dalarna, Akademin Industri och samhälle, Byggteknik.
    Han, Mengjie
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Chen, Xiangjie
    Yuan, Yanping
    Techno-economic analysis of a solar photovoltaic/thermal (PV/T) concentrator for building application in Sweden using Monte Carlo method2018Ingår i: Energy Conversion and Management, ISSN 0196-8904, E-ISSN 1879-2227, Vol. 165, s. 8-24Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The solar energy share in Sweden will grow up significantly in next a few decades. Such transition offers not only great opportunity but also uncertainties for the emerging solar photovoltaic/thermal (PV/T) technologies. This paper therefore aims to conduct a techno-economic evaluation of a reference solar PV/T concentrator in Sweden for building application. An analytical model is developed based on the combinations of Monte Carlo simulation techniques and multi energy-balance/financial equations, which takes into account of the integrated uncertainties and risks of various variables. In the model, 11 essential input variables, i.e. average daily solar irradiance, electrical/thermal efficiency, prices of electricity/heating, operation & management (OM) cost, PV/T capital cost, debt to equity ratio, interest rate, discount rate, and inflation rate, are considered, while the economic evaluation metrics, such as levelized cost of energy (LCOE), net present value (NPV), and payback period (PP), are primarily assessed. According to the analytical results, the mean values of LCOE, NPV and PP of the reference PV/T connector are observed at 1.27 SEK/kW h (0.127 €/kW h), 18,812.55 SEK (1881.255 €) and 10 years during its 25 years lifespan, given the project size at 10.37 m2 and capital cost at 4482–5378 SEK/m2 (448.2–537.8 €/m2). The positive NPV indicates that the investment on the selected PV/T concentrator will be profitable as the projected earnings exceeds the anticipated costs, depending on the NPV decision rule. The sensitivity analysis and the parametric study illustrate that the economic performance of the reference PV/T concentrator in Sweden is mostly proportional to solar irradiance, debt to equity ratio and heating price, but disproportionate to capital cost and discount rate. Together with additional market analysis of PV/T technologies in Sweden, it is expected that this paper could clarify the economic situation of PV/T technologies in Sweden and provide a useful model for their further investment decisions, in order to achieve sustainable and low-carbon economics, with an expanded quantitative discussion of the real economic or policy scenarios that may lead to those outcomes.

  • 41.
    Carling, Kenneth
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Han, Mengjie
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    GRASP and statistical bounds for heuristic solutions to combinatorial problems2016Rapport (Övrigt vetenskapligt)
    Abstract [en]

    The quality of a heuristic solution to a NP-hard combinatorial problem is hard to assess. A few studies have advocated and tested statistical bounds as a method for assessment. These studies indicate that statistical bounds are superior to the more widely known and used deterministic bounds. However, the previous studies have been limited to a few metaheuristics and combinatorial problems and, hence, the general performance of statistical bounds in combinatorial optimization remains an open question. This work complements the existing literature on statistical bounds by testing them on the metaheuristic Greedy Randomized Adaptive Search Procedures (GRASP) and four combinatorial problems. Our findings confirm previous results that statistical bounds are reliable for the p-median problem, while we note that they also seem reliable for the set covering problem. For the quadratic assignment problem, the statistical bounds has previously been found reliable when obtained from the Genetic algorithm whereas in this work they found less reliable. Finally, we provide statistical bounds to four 2-path network design problem instances for which the optimum is currently unknown.

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  • 42.
    Han, Mengjie
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik. HUI Research.
    Håkansson, Johan
    Högskolan Dalarna, Akademin Industri och samhälle, Informatik. Högskolan Dalarna, Akademin Industri och samhälle, Kulturgeografi. HUI Research, Stockholm.
    Rönnegård, Lars
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik. HUI Research, Stockholm.
    To what extent do neighbouring populations affect local population growth over time?2016Ingår i: Population, Space and Place, ISSN 1544-8444, E-ISSN 1544-8452, Vol. 22, nr 1, s. 68-83Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This study covers a period when society changed from a pre-industrial agricultural society to a post-industrial service-producing society. Parallel with this social transformation, major population changes took place. In this study, we analyse to what extent local population change is affected by neighbouring populations. To do this, we focused on the last 190 years of local population change that redistributed population in Sweden. We used literature to identify several different processes in the population redistribution. The different processes implied different spatial dependencies between local population change and the surrounding populations. The analysis is based on an unchanged historical parish division, and we used an index of local spatial correlation to describe different types of spatial dependencies that influenced the redistribution of the population. To control inherent time dependencies, we introduced a non-separable spatial-temporal correlation model into the analysis of population redistribution. Hereby, several different spatial dependencies could be simultaneously observed over time. The main conclusions are that while local population changes have been highly dependent on neighbouring populations in the 19th century, this spatial dependence became insignificant already when two parishes are separated by 5 km in the late 20th century. It is argued that the only process that significantly redistributed the population at the end of the 20th century is the immigration to Sweden.

  • 43.
    Carling, Kenneth
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Han, Mengjie
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Håkansson, Johan
    Högskolan Dalarna, Akademin Industri och samhälle, Informatik.
    Rebreyend, Pascal
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Distance measure and the p-median problem in rural areas2015Ingår i: Annals of Operations Research, ISSN 0254-5330, E-ISSN 1572-9338, Vol. 226, nr 1, s. 89-99Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The p-median model is used to locate P facilities to serve a geographically distributed population. Conventionally, it is assumed that the population patronize the nearest facility and that the distance between the resident and the facility may be measured by the Euclidean distance. Carling, Han, and Håkansson (2012) compared two network distances with the Euclidean in a rural region with a sparse, heterogeneous network and a non-symmetric distribution of the population. For a coarse network and P small, they found, in contrast to the literature, the Euclidean distance to be problematic. In this paper we extend their work by use of a refined network and study systematically the case when P is of varying size (1-100 facilities). We find that the network distance give as good a solution as the travel-time network. The Euclidean distance gives solutions some 4-10 per cent worse than the network distances, and the solutions tend to deteriorate with increasing P. Our conclusions extend to intra-urban location problems.

  • 44.
    Carling, Kenneth
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Han, Mengjie
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Håkansson, Johan
    Högskolan Dalarna, Akademin Industri och samhälle, Informatik. Högskolan Dalarna, Akademin Industri och samhälle, Kulturgeografi.
    Meng, Xiangli
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Rudholm, Niklas
    Högskolan Dalarna, Akademin Industri och samhälle, Nationalekonomi.
    Measuring transport related CO2 emissions induced by online and brick-and-mortar retailing2015Ingår i: Transportation Research Part D: Transport and Environment, ISSN 1361-9209, E-ISSN 1879-2340, Vol. 40, s. 28-42Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    We develop a method for empirically measuring the difference in transport related carbon footprint between traditional and online retailing (“e-tailing”) from entry point to a geographical area to consumer residence. The method only requires data on the locations of brick-and-mortar stores, online delivery points, and residences of the region’s population, and on the goods transportation networks in the studied region. Such data are readily available in most countries. The method has been evaluated using data from the Dalecarlia region in Sweden, and is shown to be robust to all assumptions made. In our empirical example, the results indicate that the average distance from consumer residence to a brick-and-mortar retailer is 48.54 km in the studied region, while the average distance to an online delivery point is 6.7 km. The results also indicate that e-tailing increases the average distance traveled from the regional entry point to the delivery point from 47.15 km for a brick-and-mortar store to 122.75 km for the online delivery points. However, as professional carriers transport the products in bulk to stores or online delivery points, which is more efficient than consumers’ transporting the products to their residences, the results indicate that consumers switching from traditional to e-tailing on average reduce their transport CO2 footprints by 84% when buying standard consumer electronics products. 

  • 45.
    Carling, Kenneth
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Han, Mengjie
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Håkansson, Johan
    Högskolan Dalarna, Akademin Industri och samhälle, Informatik.
    Rebreyend, Pascal
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Testing the gravity p-median model empirically2015Ingår i: Operations Research Perspectives, ISSN 2214-7160, Vol. 2, nr 124, artikel-id 132Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Regarding the location of a facility, the presumption in the widely used p-median model is that the customer opts for the shortest route to the nearest facility. However, this assumption is problematic on free markets since the customer is presumed to gravitate to a facility by the distance to and the attractiveness of it. The recently introduced gravity p-median model offers an extension to the p-median model that account for this. The model is therefore potentially interesting, although it has not yet been implemented and tested empirically. In this paper, we have implemented the model in an empirical problem of locating vehicle inspections, locksmiths, and retail stores of vehicle spare-parts for the purpose of investigating its superiority to the p-median model. We found, however, the gravity p-median model to be of limited use for the problem of locating facilities as it either gives solutions similar to the p-median model, or it gives unstable solutions due to a non-concave objective function.

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  • 46.
    Han, Mengjie
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Håkansson, Johan
    Högskolan Dalarna, Akademin Industri och samhälle, Informatik.
    Rebreyend, Pascal
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    How does data quality in a network affect heuristic solutions?2014Rapport (Övrigt vetenskapligt)
    Abstract [en]

    To have good data quality with high complexity is often seen to be important. Intuition says that the higher accuracy and complexity the data have the better the analytic solutions becomes if it is possible to handle the increasing computing time. However, for most of the practical computational problems, high complexity data means that computational times become too long or that heuristics used to solve the problem have difficulties to reach good solutions. This is even further stressed when the size of the combinatorial problem increases. Consequently, we often need a simplified data to deal with complex combinatorial problems. In this study we stress the question of how the complexity and accuracy in a network affect the quality of the heuristic solutions for different sizes of the combinatorial problem. We evaluate this question by applying the commonly used

    p-median model, which is used to find optimal locations in a network of p supply points that serve n demand points. To evaluate this, we vary both the accuracy (the number of nodes) of the network and the size of the combinatorial problem (p).

    The investigation is conducted by the means of a case study in a region in Sweden with an asymmetrically distributed population (15,000 weighted demand points), Dalecarlia. To locate 5 to 50 supply points we use the national transport administrations official road network (NVDB). The road network consists of 1.5 million nodes. To find the optimal location we start with 500 candidate nodes in the network and increase the number of candidate nodes in steps up to 67,000 (which is aggregated from the 1.5 million nodes). To find the optimal solution we use a simulated annealing algorithm with adaptive tuning of the temperature. The results show that there is a limited

    improvement in the optimal solutions when the accuracy in the road network increase and the combinatorial problem (low

    p) is simple. When the combinatorial problem is complex (large p) the improvements of increasing the accuracy in the road network are much larger. The results also show that choice of the best accuracy of the network depends on the complexity of the combinatorial (varying p) problem.

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    How does data quality in a network affect heuristic solutions?
  • 47.
    Rebreyend, Pascal
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Datateknik.
    Han, Mengjie
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Håkansson, Johan
    Högskolan Dalarna, Akademin Industri och samhälle, Kulturgeografi.
    How does different algorithm work when applied on the different road networks when optimal location of facilities is searched for in rural areas?2014Ingår i: Web Information Systems Engineering – WISE 2013 Workshops: WISE 2013 International Workshops BigWebData, MBC, PCS, STeH, QUAT, SCEH, and STSC 2013, Nanjing, China, October 13-15, 2013, Revised Selected Papers / [ed] Zhisheng Huang, Chengfei Liu, Jing He, Guangyan Huang, Berlin: Springer Berlin/Heidelberg, 2014, Vol. 8182, s. 284-291Konferensbidrag (Refereegranskat)
    Abstract [en]

    The p-median problem is often used to locate P service facilities in a geographically distributed population. Important for the performance of such a model is the distance measure. The first aim in this study is to analyze how the optimal location solutions vary, using the p-median model, when the road network is alternated. It is hard to find an exact optimal solution for p-median problems. Therefore, in this study two heuristic solutions are applied, simulating annealing and a classic heuristic. The secondary aim is to compare the optimal location solutions using different algorithms for large p-median problem. The investigation is conducted by the means of a case study in a rural region with a. asymmetrically distributed population, Dalecarlia. The study shows that the use of more accurate road networks gives better solutions for optimal location, regardless what algorithm that is used and regardless how many service facilities that is opt for. It is also shown that the Simulating annealing algorithm not just is much faster than the classic heuristic used here, but also in most cases gives better solutions.

  • 48.
    Carling, Kenneth
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Han, Mengjie
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Håkansson, Johan
    Högskolan Dalarna, Akademin Industri och samhälle, Informatik. Högskolan Dalarna, Akademin Industri och samhälle, Kulturgeografi.
    Meng, Xiangli
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Rudholm, Niklas
    Högskolan Dalarna, Akademin Industri och samhälle, Nationalekonomi. HUI Research.
    Measuring CO2 emissions induced by online and brick-and-mortar retailing2014Rapport (Övrigt vetenskapligt)
    Abstract [en]

    We develop a method for empirically measuring the difference in carbon footprint between traditional and online retailing (“e-tailing”) from entry point to a geographical area to consumer residence. The method only requires data on the locations of brick-and-mortar stores, online delivery points, and residences of the region’s population, and on the goods transportation networks in the studied region. Such data are readily available in most countries, so the method is not country or region specific. The method has been evaluated using data from the Dalecarlia region in Sweden, and is shown to be robust to all assumptions made. In our empirical example, the results indicate that the average distance from consumer residence to a brick-and-mortar retailer is 48.54 km in the studied region, while the average distance to an online delivery point is 6.7 km. The results also indicate that e-tailing increases the average distance traveled from the regional entry point to the delivery point from 47.15 km for a brick-and-mortar store to 122.75 km for the online delivery points. However, as professional carriers transport the products in bulk to stores or online delivery points, which is more efficient than consumers’ transporting the products to their residences, the results indicate that consumers switching from traditional to e-tailing on average reduce their CO2 footprints by 84% when buying standard consumer electronics products. 

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  • 49.
    Carling, Kenneth
    et al.
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Han, Mengjie
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Håkansson, Johan
    Högskolan Dalarna, Akademin Industri och samhälle, Informatik. Högskolan Dalarna, Akademin Industri och samhälle, Kulturgeografi.
    Meng, Xiangli
    Högskolan Dalarna, Akademin Industri och samhälle, Statistik.
    Rudholm, Niklas
    Högskolan Dalarna, Akademin Industri och samhälle, Nationalekonomi. HUI Research.
    Measuring CO2 emissions induced by online and brick-and-mortar retailing2014Rapport (Övrigt vetenskapligt)
    Abstract [en]

    We develop a method for empirically measuring the difference in carbon footprint between traditional and online retailing (“e-tailing”) from entry point to a geographical area to consumer residence. The method only requires data on the locations of brick-and-mortar stores, online delivery points, and residences of the region’s population, and on the goods transportation networks in the studied region. Such data are readily available in most countries, so the method is not country or region specific. The method has been evaluated using data from the Dalecarlia region in Sweden, and is shown to be robust to all assumptions made. In our empirical example, the results indicate that the average distance from consumer residence to a brick-and-mortar retailer is 48.54 km in the studied region, while the average distance to an online delivery point is 6.7 km. The results also indicate that e-tailing increases the average distance traveled from the regional entry point to the delivery point from 47.15 km for a brick-and-mortar store to 122.75 km for the online delivery points. However, as professional carriers transport the products in bulk to stores or online delivery points, which is more efficient than consumers’ transporting the products to their residences, the results indicate that consumers switching from traditional to e-tailing on average reduce their CO2 footprints by 84% when buying standard consumer electronics products. 

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    fulltext
  • 50.
    Han, Mengjie
    Högskolan Dalarna, Akademin Industri och samhälle, Mikrodataanalys.
    Heuristic optimization of the p-median problem and population re-distribution2013Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
    Abstract [en]

    This thesis contributes to the heuristic optimization of the p-median problem and Swedish population redistribution.  

    The p-median model is the most representative model in the location analysis. When facilities are located to a population geographically distributed in Q demand points, the p-median model systematically considers all the demand points such that each demand point will have an effect on the decision of the location. However, a series of questions arise. How do we measure the distances? Does the number of facilities to be located have a strong impact on the result? What scale of the network is suitable? How good is our solution? We have scrutinized a lot of issues like those. The reason why we are interested in those questions is that there are a lot of uncertainties in the solutions. We cannot guarantee our solution is good enough for making decisions. The technique of heuristic optimization is formulated in the thesis.  

    Swedish population redistribution is examined by a spatio-temporal covariance model. A descriptive analysis is not always enough to describe the moving effects from the neighbouring population. A correlation or a covariance analysis is more explicit to show the tendencies. Similarly, the optimization technique of the parameter estimation is required and is executed in the frame of statistical modeling. 

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