Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Clustering Nearly Zero Energy Buildings for Improved Performance
Collaborations among nZEBs (e.g., renewable energy sharing) can improve nZEBs’ performance at the community level. To enable such collaborations, the nZEBs need to be properly grouped. Grouping nZEBs with similar energy characteristics merely brings limited benefits due to limited collaboration existed, while grouping nZEBs with diverse energy characteristics can bring more benefits. In the planning of nZEB communities, due to the large diversity of energy characteristics and computation complexity, proper grouping that maximizes the collaboration benefits is difficult, and such a grouping method is still lacking. Therefore, this chapter proposes a clustering-based grouping method to improve nZEB performance. Using the field data, the grouping method first identifies the representative energy characteristics by advanced clustering algorithms. Then, it searches the optimal grouping alternative of these representative profiles that has the optimal performance. For validation, the proposed grouping method is compared with two cases (the nZEBs are either not grouped or randomly grouped) in aspects of economic costs and grid interaction. The study results show that the developed method is effective in improving nZEBs’ performance at the community level. The proposed method will provide the decision makers a means to group nZEBs, which maximizes the collaboration benefits and thus assists the planning of nZEB communities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.
Clustering Nearly Zero Energy Buildings for Improved Performance
Collaborations among nZEBs (e.g., renewable energy sharing) can improve nZEBs’ performance at the community level. To enable such collaborations, the nZEBs need to be properly grouped. Grouping nZEBs with similar energy characteristics merely brings limited benefits due to limited collaboration existed, while grouping nZEBs with diverse energy characteristics can bring more benefits. In the planning of nZEB communities, due to the large diversity of energy characteristics and computation complexity, proper grouping that maximizes the collaboration benefits is difficult, and such a grouping method is still lacking. Therefore, this chapter proposes a clustering-based grouping method to improve nZEB performance. Using the field data, the grouping method first identifies the representative energy characteristics by advanced clustering algorithms. Then, it searches the optimal grouping alternative of these representative profiles that has the optimal performance. For validation, the proposed grouping method is compared with two cases (the nZEBs are either not grouped or randomly grouped) in aspects of economic costs and grid interaction. The study results show that the developed method is effective in improving nZEBs’ performance at the community level. The proposed method will provide the decision makers a means to group nZEBs, which maximizes the collaboration benefits and thus assists the planning of nZEB communities. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.
Clustering Nearly Zero Energy Buildings for Improved Performance
Huang, Pei (Autor:in) / Sun, Yongjun (Autor:in)
01.01.2023
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
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