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  • 1. Bee, Elena
    et al.
    Prada, Alessandro
    Baggio, Paolo
    Psimopoulos, Emmanouil
    Högskolan Dalarna, Akademin Industri och samhälle, Energiteknik. Uppsala University.
    Air-source heat pump and photovoltaic systems for residential heating and cooling: Potential of self-consumption in different European climates2019Ingår i: Building Simulation, ISSN 1996-3599, E-ISSN 1996-8744, Vol. 12, nr 3, s. 453-463Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Renewable sources will play a key role in meeting the EU targets for 2030. The combined use of an aerothermal source through a heat pump and a solar source with a photovoltaic (PV) system is one feasible and promising technology for the heating and cooling of residential spaces. In this study, a detailed model of a single-family house with an air-source heat pump and a PV system is developed with the TRNSYS simulation software. Yearly simulations are run for two types of buildings and nine European climates, for both heating and cooling (where needed), in order to have an overview of the system behaviour, which is deeply influenced by the climate. The storage system (electrical and thermal) is also investigated, by means of multiple simulation scenarios, with and without the battery and with different water storage sizes. The numerical results provide an overview of the performance of the considered heating and cooling system, as well as the balance of the electrical energy exchange between the grid, the building, and the PV array.

  • 2. Pan, Song
    et al.
    Wang, Xinru
    Wei, Yixuan
    Zhang, Xingxing
    Högskolan Dalarna, Akademin Industri och samhälle, Energiteknik.
    Gál, Csilla V
    Högskolan Dalarna, Akademin Industri och samhälle, Energiteknik.
    Rend, Guangying
    Yan, Da
    Yong, Shi
    Wu, Jinshun
    Liu, Jiaping
    Cluster analysis for occupant-behavior based electricity load patterns in buildings: a case study in Shanghai residences2017Ingår i: Building Simulation, ISSN 1996-3599, E-ISSN 1996-8744, Vol. 10, nr 6, s. 889-898Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    In building performance simulation, occupant behavior contributes to large uncertainties, which often lead to considerable discrepancies between actual energy consumption and simulation results. This paper aims to extract occupant-behavior related electricity load patterns using classical K-means clustering approach at the initial investigation stage. Smart-metering data from a case study in Shanghai, China, was used for the load pattern analysis. The electricity load patterns of occupants were examined on a daily/weekly/seasonal basis. According to their load patterns, occupants were categorized as (a) white-collar workers, (b) poor or older families and (c) rich or young families. The daily patterns indicated that electricity use was much more random and fluctuated over a wide range. Most households of the monitored communities consumed relatively-low electricity; the characteristic double peak with higher level of consumption in the morning and evening were only apparent in a relatively small subset of residents (mostly white-collar workers). The weekly analysis found that significant load shifting towards weekend days occurred in the poor or old family group. The electricity saving potential was greatest in the white-collar workers and the rich or young family groups. This study concludes with recommendations to stakeholders utilizing our load profiling results. The research provides a rare insight into the electricity-use-related occupant behaviors of Shanghai residents through the case study of two communities. The findings of the study are also presented in a meaningful way so that they can directly aid the decision-making of governments and other stakeholders interested in energy efficiency. The research results are also relevant to the building energy simulation community as they are derived from observations, and thus can have the potential to improve the efficiency and accuracy of numerical simulation results.

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