An Innovative Decision Support System to Improve the Energy Efficiency of Buildings in Urban Areas

被引:41
|
作者
Sztubecka, Malgorzata [1 ]
Skiba, Marta [2 ]
Mrowczynska, Maria [2 ]
Bazan-Krzywoszanska, Anna [2 ]
机构
[1] UTP Univ Sci & Technol, Fac Civil & Environm Engn & Architecture, Ul Prof S Kaliskiego 7, PL-85796 Bydgoszcz, Poland
[2] Univ Zielona Gora, Fac Civil Engn Architecture & Environm Engn, Ul Prof Z Szafrana 1, PL-65516 Zielona Gora, Poland
关键词
energy efficiency of buildings; energy potential; renewable energy sources; Geographic Information System; multi-criteria analysis; smart city; urban analysis; OPTIMIZATION; MANAGEMENT; TOOLS; HEAT;
D O I
10.3390/rs12020259
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Improving in the energy efficiency of urban buildings, and maximizing the savings and the resulting benefits require information support from city decision-makers, planners, and designers. The selection of the appropriate analytical methods will allow them to make optimal design and location decisions. Therefore, the research problem of this article is the development of an innovative decision support system using multi-criteria analysis and Geographic Information Systems (decision support system + Geographic Information Systems = DGIS) for planning urban development. The proposed decision support system provides information to energy consumers about the location of energy efficiency improvement potential. This potential has been identified as the possibility of introducing low-energy buildings and the use of renewable energy sources. DGIS was tested in different construction areas (categories: A, B, C, D), Zielona Gora quarters. The results showed which area among the 53 quarters with a separate dominant building category was the most favorable for increasing energy efficiency, and where energy efficiency could be improved by investing in renewable energy sources, taking into account the decision-maker. The proposed DGIS system can be used by local decision-makers, allowing better action to adapt cities to climate change and to protect the environment. This approach is part of new data processing strategies to build the most favorable energy scenarios in urban areas.
引用
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页数:19
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