Energy Demand and Supply Simultaneous Optimization to Design a Nearly Zero-Energy House

被引:18
|
作者
Ferrara, Maria [1 ]
Prunotto, Federico [1 ]
Rolfo, Andrea [1 ]
Fabrizio, Enrico [1 ,2 ]
机构
[1] Politecn Torino, Dept Energy, I-10129 Turin, Italy
[2] FULL, I-10134 Turin, Italy
来源
APPLIED SCIENCES-BASEL | 2019年 / 9卷 / 11期
关键词
nearly zero-energy buildings; simulation; optimization; design; TRNSYS; GenOpt; Italy; Mediterranean climate; EPBD; 2018; 844; EU; MEDITERRANEAN CLIMATE; SYSTEMS; SIMULATION; COMFORT; NZEB;
D O I
10.3390/app9112261
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
Featured Application The results of this paper can support the design of nearly zero-energy single-family houses in the Italian context. The provided methodological framework can support the design of nearly zero-energy buildings and be replicated in different contexts. Abstract The effective design of nearly zero-energy buildings depends on a large set of interdependent variables, which affect both energy demand and supply. Considering them simultaneously is fundamental when searching for optimal design of nearly zero-energy buildings, as encouraged by the EU in the second recast of the Energy Performance of Building Directive (EPBD). This paper presents the application of the new energy demand and supply simultaneous optimization (EDeSSOpt) methodology to optimize the design of a single-family house in the Italian context. Both primary energy optimization and financial optimization are carried out in the context of European regulations. Robustness of the resulting optimal solution is studied through analysis of optimum neighborhoods. The resulting cost-optimized solution relies on a moderately insulated envelope, a highly efficient system, and 34% of coverage from renewables. The energy-optimized solution requires a higher level of insulation and a higher coverage from renewables, demonstrating that there is still a gap between energy and cost optimums. Beyond the results, integrated optimization by means of EDeSSOpt is demonstrated to better minimize cost functions while improving the robustness of results.
引用
收藏
页数:21
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