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Publications (10 of 17) Show all publications
Li, Y., Rezgui, Y., Guerriero, A., Zhang, X., Han, M., Kubicki, S. & Yan, D. (2020). Development of an adaptation table to enhance the accuracy of the predicted mean vote model. Building and Environment, 168, Article ID 106504.
Open this publication in new window or tab >>Development of an adaptation table to enhance the accuracy of the predicted mean vote model
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2020 (English)In: Building and Environment, ISSN 0360-1323, E-ISSN 1873-684X, Vol. 168, article id 106504Article in journal (Refereed) Published
National Category
Building Technologies
Research subject
Energy and Built Environments
Identifiers
urn:nbn:se:du-31070 (URN)10.1016/j.buildenv.2019.106504 (DOI)2-s2.0-85074601389 (Scopus ID)
Available from: 2019-11-01 Created: 2019-11-01 Last updated: 2019-11-26Bibliographically approved
Han, M., May, R., Zhang, X., Wang, X., Pan, S., Yan, D., . . . Xu, L. (2019). A review of reinforcement learning methodologies for controlling occupant comfort in buildings. Sustainable cities and society, 51, Article ID 101748.
Open this publication in new window or tab >>A review of reinforcement learning methodologies for controlling occupant comfort in buildings
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2019 (English)In: Sustainable cities and society, ISSN 2210-6707, Vol. 51, article id 101748Article in journal (Refereed) Published
National Category
Building Technologies
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-30601 (URN)2-s2.0-85070980900 (Scopus ID)
Available from: 2019-08-08 Created: 2019-08-08 Last updated: 2019-10-11Bibliographically approved
Zhang, X., Wu, J., Pan, S. & Han, M. (2019). An economic analysis of the solar photovoltaic/thermal (PV/T) technologies in Sweden: A case study. In: IOP Conference Series: Materials Science and Engineering. Paper presented at Solaris 2018, The 9th edition of the international SOLARIS conference30th-31st of August, 2018, Chengdu, China. , 556(1), Article ID 012002.
Open this publication in new window or tab >>An economic analysis of the solar photovoltaic/thermal (PV/T) technologies in Sweden: A case study
2019 (English)In: IOP Conference Series: Materials Science and Engineering, 2019, Vol. 556, no 1, article id 012002Conference paper, Published paper (Refereed)
National Category
Energy Engineering
Research subject
Energy and Built Environments
Identifiers
urn:nbn:se:du-30821 (URN)10.1088/1757-899X/556/1/012002 (DOI)2-s2.0-85072132529 (Scopus ID)
Conference
Solaris 2018, The 9th edition of the international SOLARIS conference30th-31st of August, 2018, Chengdu, China
Available from: 2019-09-27 Created: 2019-09-27 Last updated: 2019-09-27Bibliographically approved
Jin, Y., Yan, D., Zhang, X., Han, M., Kang, X., An, J. & Sun, H. (2019). District household electricity consumption pattern analysis based on auto-encoder algorithm. In: IOP Conference Series: Materials Science and Engineering: . Paper presented at 10th International Conference on Indoor Air Quality, Ventilation and Energy Conservation in Buildings, IAQVEC 2019; Bari; Italy; 5 September 2019 through 7 September 2019; Code 153083. , 609(7), Article ID 072028.
Open this publication in new window or tab >>District household electricity consumption pattern analysis based on auto-encoder algorithm
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2019 (English)In: IOP Conference Series: Materials Science and Engineering, 2019, Vol. 609, no 7, article id 072028Conference paper, Published paper (Refereed)
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.

Series
IOP Conference Series: Materials Science and Engineering, ISSN 17578981
National Category
Civil Engineering
Research subject
Energy and Built Environments
Identifiers
urn:nbn:se:du-31169 (URN)10.1088/1757-899X/609/7/072028 (DOI)2-s2.0-85074698362 (Scopus ID)
Conference
10th International Conference on Indoor Air Quality, Ventilation and Energy Conservation in Buildings, IAQVEC 2019; Bari; Italy; 5 September 2019 through 7 September 2019; Code 153083
Available from: 2019-12-06 Created: 2019-12-06 Last updated: 2019-12-06
Han, M. & Carling, K. (2019). GRASP and Statistical Bounds for Heuristic Solutions to Combinatorial Problems. International Journal of Management and Applied Science, 5(8), 113-119
Open this publication in new window or tab >>GRASP and Statistical Bounds for Heuristic Solutions to Combinatorial Problems
2019 (English)In: International Journal of Management and Applied Science, ISSN 2394-7926, Vol. 5, no 8, p. 113-119Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Institute of Research and Journals (IRAJ), 2019
Keywords
Combinatorial Problems, GRASP, Statistical Bounds, Statistical Optimum Estimation Techniques
National Category
Other Computer and Information Science
Research subject
Complex Systems – Microdata Analysis, General Microdata Analysis - methods
Identifiers
urn:nbn:se:du-31077 (URN)
Available from: 2019-11-05 Created: 2019-11-05 Last updated: 2019-11-08Bibliographically approved
Han, M., Håkansson, J. & Lundmark, M. (2019). Intra-urban location of stores and labour turnover in retail. International Review of Retail Distribution & Consumer Research, 29(4), 359-375
Open this publication in new window or tab >>Intra-urban location of stores and labour turnover in retail
2019 (English)In: International Review of Retail Distribution & Consumer Research, ISSN 0959-3969, E-ISSN 1466-4402, Vol. 29, no 4, p. 359-375Article in journal (Refereed) Published
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.

Keywords
Employee turnover, commuting cost, firm internal factors, firm external factors, generalized linear modelling
National Category
Human Geography Probability Theory and Statistics
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-29862 (URN)10.1080/09593969.2019.1591480 (DOI)2-s2.0-85063257645 (Scopus ID)
Available from: 2019-04-08 Created: 2019-04-08 Last updated: 2019-11-26Bibliographically approved
Wei, Y., Xia, L., Pan, S., Wu, J., Zhang, X., Han, M., . . . Li, Q. (2019). Prediction of occupancy level and energy consumption in office building using blind system identification and neural networks. Applied Energy, 240, 276-294
Open this publication in new window or tab >>Prediction of occupancy level and energy consumption in office building using blind system identification and neural networks
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2019 (English)In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 240, p. 276-294Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Elsevier, 2019
Keywords
Occupancy estimation; Blind system identification (BSI); Prediction model for energy consumption; Feedforward neural network; Extreme learning machine
National Category
Energy Engineering
Research subject
Energy and Built Environments
Identifiers
urn:nbn:se:du-29562 (URN)10.1016/j.apenergy.2019.02.056 (DOI)000468714300020 ()
Available from: 2019-02-24 Created: 2019-02-24 Last updated: 2019-08-26Bibliographically approved
May, R., Zhang, X., Wu, J. & Han, M. (2019). Reinforcement learning control for indoor comfort: A survey. In: IOP Conference Series: Materials Science and Engineering: . Paper presented at 10th International Conference on Indoor Air Quality, Ventilation and Energy Conservation in Buildings, IAQVEC 2019; Bari; Italy; 5 September 2019 through 7 September 2019; Code 153083. , 609(6), Article ID 062011.
Open this publication in new window or tab >>Reinforcement learning control for indoor comfort: A survey
2019 (English)In: IOP Conference Series: Materials Science and Engineering, 2019, Vol. 609, no 6, article id 062011Conference paper, Published paper (Refereed)
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.

National Category
Civil Engineering Computer and Information Sciences
Research subject
Complex Systems – Microdata Analysis
Identifiers
urn:nbn:se:du-31167 (URN)10.1088/1757-899X/609/6/062011 (DOI)2-s2.0-85074523665 (Scopus ID)
Conference
10th International Conference on Indoor Air Quality, Ventilation and Energy Conservation in Buildings, IAQVEC 2019; Bari; Italy; 5 September 2019 through 7 September 2019; Code 153083
Available from: 2019-12-06 Created: 2019-12-06 Last updated: 2019-12-06
Wei, Y., Zhang, X., Shi, Y., Xia, L., Pan, S., Wu, J., . . . Zhao, X. (2018). A review of data-driven approaches for prediction and classification of building energy consumption. Renewable & sustainable energy reviews, 82(1), 1027-1047
Open this publication in new window or tab >>A review of data-driven approaches for prediction and classification of building energy consumption
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2018 (English)In: Renewable & sustainable energy reviews, ISSN 1364-0321, E-ISSN 1879-0690, Vol. 82, no 1, p. 1027-1047Article in journal (Refereed) Published
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.

Place, publisher, year, edition, pages
Elsevier, 2018
Keywords
Data driven approach, Building, Energy consumption, Prediction, Classification
National Category
Energy Engineering
Research subject
Energy, Forests and Built Environments
Identifiers
urn:nbn:se:du-26385 (URN)10.1016/j.rser.2017.09.108 (DOI)2-s2.0-85030703701 (Scopus ID)
Available from: 2017-10-09 Created: 2017-10-09 Last updated: 2019-08-26Bibliographically approved
Han, M., Zhang, X., Xu, L., May, R., Pan, S. & Wu, J. (2018). A review of reinforcement learning methodologies on control systems for building energy. Borlänge: Högskolan Dalarna
Open this publication in new window or tab >>A review of reinforcement learning methodologies on control systems for building energy
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2018 (English)Report (Other academic)
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.

Place, publisher, year, edition, pages
Borlänge: Högskolan Dalarna, 2018. p. 26
Series
Working papers in transport, tourism, information technology and microdata analysis, ISSN 1650-5581 ; 2018:02
Keywords
Reinforcement learning; Markov decision processes; building energy; control; multi-agent system
National Category
Control Engineering
Research subject
Complex Systems – Microdata Analysis, General Microdata Analysis - methods
Identifiers
urn:nbn:se:du-27956 (URN)
Available from: 2018-06-19 Created: 2018-06-19 Last updated: 2018-06-20Bibliographically approved
Organisations
Identifiers
ORCID iD: ORCID iD iconorcid.org/0000-0003-4212-8582

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