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Publications (10 of 42) Show all publications
Han, M., Canli, I., Shah, J., Zhang, X., Dino, I. G. & Kalkan, S. (2024). Perspectives of Machine Learning and Natural Language Processing on Characterizing Positive Energy Districts. Buildings, 14(2), Article ID 371.
Open this publication in new window or tab >>Perspectives of Machine Learning and Natural Language Processing on Characterizing Positive Energy Districts
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2024 (English)In: Buildings, E-ISSN 2075-5309, Vol. 14, no 2, article id 371Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
MDPI, 2024
Keywords
Positive Energy District; machine learning; natural language processing; characterization
National Category
Energy Systems
Identifiers
urn:nbn:se:du-48012 (URN)10.3390/buildings14020371 (DOI)001172199400001 ()2-s2.0-85185706786 (Scopus ID)
Funder
Vinnova, P2022-01000Swedish Energy Agency, 8569501
Available from: 2024-02-10 Created: 2024-02-10 Last updated: 2024-03-18Bibliographically approved
Wang, Z., Liu, F., Han, M., Tang, H. & Wan, B. (2024). PML-ED: A method of partial multi-label learning by using encoder-decoder framework and exploring label correlation. Information Sciences, 661, Article ID 120165.
Open this publication in new window or tab >>PML-ED: A method of partial multi-label learning by using encoder-decoder framework and exploring label correlation
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2024 (English)In: Information Sciences, ISSN 0020-0255, E-ISSN 1872-6291, Vol. 661, article id 120165Article in journal (Refereed) Published
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.

Keywords
Partial multi-label learning, Label correlation, Label disambiguation, Encoder-Decoder framework, Conditional layer normalization
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:du-47973 (URN)10.1016/j.ins.2024.120165 (DOI)2-s2.0-85183583505 (Scopus ID)
Available from: 2024-02-05 Created: 2024-02-05 Last updated: 2024-02-05
Wang, Z., Xue, L., Guo, Y., Han, M. & Liang, S. (2024). Solving dynamic multi-objective optimization problems via quantifying intensity of environment changes and ensemble learning-based prediction strategies. Applied Soft Computing, 154, Article ID 111317.
Open this publication in new window or tab >>Solving dynamic multi-objective optimization problems via quantifying intensity of environment changes and ensemble learning-based prediction strategies
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2024 (English)In: Applied Soft Computing, ISSN 1568-4946, E-ISSN 1872-9681, Vol. 154, article id 111317Article in journal (Refereed) Published
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.

Keywords
Dynamic multi-objective optimization, Change intensity quantification, Boundary learning, Ensemble learning
National Category
Computational Mathematics
Identifiers
urn:nbn:se:du-48044 (URN)10.1016/j.asoc.2024.111317 (DOI)001178018400001 ()2-s2.0-85185463488 (Scopus ID)
Available from: 2024-02-16 Created: 2024-02-16 Last updated: 2024-03-25Bibliographically approved
Wang, Z., Zhang, W., Guo, Y., Han, M., Wan, B. & Liang, S. (2023). A multi-objective chicken swarm optimization algorithm based on dual external archive with various elites. Applied Soft Computing, 133, Article ID 109920.
Open this publication in new window or tab >>A multi-objective chicken swarm optimization algorithm based on dual external archive with various elites
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2023 (English)In: Applied Soft Computing, ISSN 1568-4946, E-ISSN 1872-9681, Vol. 133, article id 109920Article in journal (Refereed) Published
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.

Keywords
Multi-objective optimization problem; Meta-heuristic; Chicken swarm optimization; Pareto dominance
National Category
Computer Sciences Other Mathematics
Identifiers
urn:nbn:se:du-44966 (URN)10.1016/j.asoc.2022.109920 (DOI)001026652800001 ()2-s2.0-85145433514 (Scopus ID)
Available from: 2023-01-02 Created: 2023-01-02 Last updated: 2023-08-07Bibliographically approved
Wan, B., Hu, Z., Garg, H., Cheng, Y. & Han, M. (2023). An integrated group decision-making method for the evaluation of hypertension follow-up systems using interval-valued q-rung orthopair fuzzy sets. Complex & Intelligent Systems, 9(4), 4521-4554
Open this publication in new window or tab >>An integrated group decision-making method for the evaluation of hypertension follow-up systems using interval-valued q-rung orthopair fuzzy sets
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2023 (English)In: Complex & Intelligent Systems, ISSN 2199-4536, E-ISSN 2198-6053, Vol. 9, no 4, p. 4521-4554Article in journal (Refereed) Published
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.

Keywords
Interval-valued q-rung orthopair, LINMAP-ARAS decision-making method, WFA operator
National Category
Computer and Information Sciences
Identifiers
urn:nbn:se:du-45328 (URN)10.1007/s40747-022-00953-w (DOI)000915705600001 ()36694862 (PubMedID)2-s2.0-85146574680 (Scopus ID)
Available from: 2023-02-01 Created: 2023-02-01 Last updated: 2023-09-21Bibliographically approved
Zhang, X., Shah, J. & Han, M. (2023). ChatGPT for Fast Learning of Positive Energy District (PED): A Trial Testing and Comparison with Expert Discussion Results. Buildings, 13(6), Article ID 1392.
Open this publication in new window or tab >>ChatGPT for Fast Learning of Positive Energy District (PED): A Trial Testing and Comparison with Expert Discussion Results
2023 (English)In: Buildings, E-ISSN 2075-5309, Vol. 13, no 6, article id 1392Article in journal (Refereed) Published
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.

Keywords
challenge, ChatGPT, communication and dissemination, impact, PED
National Category
Software Engineering Civil Engineering
Identifiers
urn:nbn:se:du-46591 (URN)10.3390/buildings13061392 (DOI)001014319900001 ()2-s2.0-85163724827 (Scopus ID)
Available from: 2023-08-03 Created: 2023-08-03 Last updated: 2024-01-17
Han, M., Shah, J. & Zhang, X. (2023). Review of natural language processing techniques for characterizing positive energy districts. In: journal of Physics; Conference series: . Paper presented at International Conference on the Built Environment in Transition, CISBAT 2023. Institute of Physics Publishing (IOPP), 2600(8), Article ID 082024.
Open this publication in new window or tab >>Review of natural language processing techniques for characterizing positive energy districts
2023 (English)In: journal of Physics; Conference series, Institute of Physics Publishing (IOPP), 2023, Vol. 2600, no 8, article id 082024Conference paper, Published paper (Refereed)
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.

Place, publisher, year, edition, pages
Institute of Physics Publishing (IOPP), 2023
Keywords
modelling, natural language processing (NLP), NLP task, PED elements, positive energy districts, Energy efficiency, Natural language processing systems, Language processing, Modeling, Natural language processing, Natural language processing task, Natural languages, Positive energies, Positive energy district, Positive energy district element, Modeling languages
National Category
Energy Engineering
Identifiers
urn:nbn:se:du-47653 (URN)10.1088/1742-6596/2600/8/082024 (DOI)2-s2.0-85180156963 (Scopus ID)
Conference
International Conference on the Built Environment in Transition, CISBAT 2023
Available from: 2024-01-02 Created: 2024-01-02 Last updated: 2024-01-02Bibliographically approved
Shah, J., Saini, P. & Han, M. (2022). Analysis And Performance Mapping Of “Component To System” For A Parabolic Trough Collector Applied To Process Heating Applications. In: ISEC 2022: . Paper presented at International Sustainable Energy Conference 2022, 5-7 July, Graz, Austria (pp. 487-488).
Open this publication in new window or tab >>Analysis And Performance Mapping Of “Component To System” For A Parabolic Trough Collector Applied To Process Heating Applications
2022 (English)In: ISEC 2022, 2022, p. 487-488Conference paper, Published paper (Refereed)
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.

National Category
Environmental Engineering
Identifiers
urn:nbn:se:du-41798 (URN)
Conference
International Sustainable Energy Conference 2022, 5-7 July, Graz, Austria
Available from: 2022-07-01 Created: 2022-07-01 Last updated: 2023-03-17Bibliographically approved
Zhao, J., Han, M., Wang, Z. & Wan, B. (2022). Autoregressive count data modeling on mobility patterns to predict cases of COVID-19 infection. Stochastic environmental research and risk assessment (Print), 36(12), 4185-4200
Open this publication in new window or tab >>Autoregressive count data modeling on mobility patterns to predict cases of COVID-19 infection
2022 (English)In: Stochastic environmental research and risk assessment (Print), ISSN 1436-3240, E-ISSN 1436-3259, Vol. 36, no 12, p. 4185-4200Article in journal (Refereed) Published
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.

Keywords
Autoregressive model; COVID-19; Generalized linear model; Mobility; Quasi-likelihood
National Category
Probability Theory and Statistics
Identifiers
urn:nbn:se:du-41786 (URN)10.1007/s00477-022-02255-6 (DOI)000814961300002 ()35765667 (PubMedID)2-s2.0-85132697317 (Scopus ID)
Funder
Dalarna University
Available from: 2022-06-29 Created: 2022-06-29 Last updated: 2023-03-17Bibliographically approved
Huang, P., Han, M., Zhang, X., Hussain, S. A., Jayprakash Bhagat, R. & Hogarehalli Kumar, D. (2022). Characterization and optimization of energy sharing performances in energy-sharing communities in Sweden, Canada and Germany. Applied Energy, 326, Article ID 120044.
Open this publication in new window or tab >>Characterization and optimization of energy sharing performances in energy-sharing communities in Sweden, Canada and Germany
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2022 (English)In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 326, article id 120044Article in journal (Refereed) Published
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)

Place, publisher, year, edition, pages
Elsevier, 2022
Keywords
Canada, Germany, Sweden, Buildings, Costs, Design, Digital storage, Electric power transmission networks, Energy storage, Genetic algorithms, Peer to peer networks, Design optimization, Energy sharing ratio, Energy sharings, Energy system design, Energy system design optimization, Peer to peer, Peer-to-peer energy sharing, PV, alternative energy, design method, electricity, electricity generation, electricity supply, genetic algorithm, latitude, optimization, self sufficiency, Renewable energy resources, Energy sharing, Energy sharing ratio (ESR)
National Category
Energy Systems
Identifiers
urn:nbn:se:du-42849 (URN)10.1016/j.apenergy.2022.120044 (DOI)000871064000003 ()2-s2.0-85139075888 (Scopus ID)
Available from: 2022-10-17 Created: 2022-10-17 Last updated: 2023-03-17Bibliographically approved
Projects
Autokarakterisering av PED:er för digitala referenser mot iterativ processoptimering (PED-ACT)Gemensam styrning av elproduktion och elbilsladdning i bostadsområden-potential för ökad egenanvändning av solel?
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-4212-8582

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