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  • 1. Gu, Yaxiu
    et al.
    Zhang, Xingxing
    Dalarna University, School of Technology and Business Studies, Energy Technology.
    Myhren, Jonn Are
    Dalarna University, School of Technology and Business Studies, Construction.
    Han, Mengjie
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Chen, Xiangjie
    Yuan, Yanping
    Techno-economic analysis of a solar photovoltaic/thermal (PV/T) concentrator for building application in Sweden using Monte Carlo method2018In: Energy Conversion and Management, ISSN 0196-8904, E-ISSN 1879-2227, Vol. 165, p. 8-24Article in journal (Refereed)
    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.

  • 2.
    Han, Mengjie
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Heuristic optimization of the p-median problem and population re-distribution2013Doctoral thesis, comprehensive summary (Other academic)
    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. 

  • 3.
    Han, Mengjie
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Håkansson, Johan
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Lundmark, M.
    Intra-urban location of stores and labour turnover in retail2019In: International Review of Retail Distribution & Consumer Research, ISSN 0959-3969, E-ISSN 1466-4402Article in journal (Refereed)
    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.

  • 4.
    Han, Mengjie
    et al.
    Dalarna University, School of Technology and Business Studies, Statistics.
    May, Ross
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Zhang, Xingxing
    Dalarna University, School of Technology and Business Studies, Energy Technology.
    Wang, Xinru
    Pan, Song
    Yan, Da
    Jin, Yuan
    Xu, Liguo
    A review of reinforcement learning methodologies for controlling occupant comfort in buildings2019In: Sustainable cities and society, ISSN 2210-6707Article in journal (Refereed)
  • 5.
    Han, Mengjie
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Mihaescu, Oana
    HUI Research, Sweden.
    Li, Yujiao
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Rudholm, Niklas
    Dalarna University, School of Technology and Business Studies, Economics. HUI Research, Sweden.
    Comparison and one-stop shopping after big-box retail entry: a spatial difference-in-difference analysis2018In: Journal of Retailing and Consumer Services, ISSN 0969-6989, E-ISSN 1873-1384, Vol. 40, p. 175-187Article in journal (Refereed)
    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

  • 6.
    Han, Mengjie
    et al.
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Zhang, Xingxing
    Dalarna University, School of Technology and Business Studies, Energy Technology.
    Xu, Liguo
    May, Ross
    Pan, Song
    Wu, Jinshun
    A review of reinforcement learning methodologies on control systems for building energy2018Report (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.

  • 7.
    Håkansson, Johan
    et al.
    Dalarna University, School of Technology and Business Studies, Human Geography.
    Carling, Kenneth
    Dalarna University, School of Technology and Business Studies, Statistics.
    Han, Mengjie
    Dalarna University, School of Technology and Business Studies, Statistics.
    Does euclidian distance work when location models are applied in rural areas?2010Report (Other academic)
  • 8. Pan, S
    et al.
    Xiong, Y
    Han, Y
    Zhang, Xingxing
    Dalarna University, School of Technology and Business Studies, Energy Technology.
    Xia, L
    Wei, S
    Wu, J
    Han, Mengjie
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    A study on influential factors of occupant window-opening behavior in an office building in China2018In: Building and Environment, ISSN 0360-1323, E-ISSN 1873-684X, Vol. 133, p. 41-50Article in journal (Refereed)
    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.

  • 9. Wei, Yixuan
    et al.
    Xia, Liang
    Pan, Song
    Wu, Jinshun
    Zhang, Xingxing
    Dalarna University, School of Technology and Business Studies, Energy Technology.
    Han, Mengjie
    Dalarna University, School of Technology and Business Studies, Statistics.
    Zhang, Weiya
    Xie, Jingchao
    Li, Qingping
    Prediction of occupancy level and energy consumption in office building using blind system identification and neural networks2019In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 240, p. 276-294Article in journal (Refereed)
    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.

  • 10. Wei, Yixuan
    et al.
    Zhang, Xingxing
    Dalarna University, School of Technology and Business Studies, Energy Technology.
    Shi, Yong
    Xia, Liang
    Pan, Song
    Wu, Jinshun
    Han, Mengjie
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Zhao, Xiaoyun
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    A review of data-driven approaches for prediction and classification of building energy consumption2018In: Renewable & sustainable energy reviews, ISSN 1364-0321, E-ISSN 1879-0690, Vol. 82, no 1, p. 1027-1047Article in journal (Refereed)
    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.

  • 11.
    Zhang, Xingxing
    et al.
    Dalarna University, School of Technology and Business Studies, Energy Technology.
    Lovati, Marco
    Vigna, Ilaria
    Widén, Joakim
    Han, Mengjie
    Dalarna University, School of Technology and Business Studies, Microdata Analysis.
    Gál, Csilla V
    Dalarna University, School of Technology and Business Studies, Energy Technology.
    Feng, Tao
    A review of urban energy systems at building cluster level incorporating renewable-energy-source (RES) envelope solutions2018In: Applied Energy, ISSN 0306-2619, E-ISSN 1872-9118, Vol. 230, p. 1034-1056Article in journal (Refereed)
    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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