Weather-data-based control of space heating operation via multi-objective optimization: Application to Italian residential buildings

作     者：Ascione, Fabrizio Bianco, Nicola Mauro, Gerardo Maria Napolitan, Davide Ferdinando Vanoli, Giuseppe Peter 

作者机构：Univ Napoli Federico II Dept Ind Engn Piazzale Tecchio 80 I-80125 Naples Italy Univ Sannio Dept Engn Piazza Roma 21 I-82100 Benevento Italy Univ Bergamo Via Salvecchio 19 I-24129 Bergamo Italy Univ Molise Dept Med Via Cesare Gazzani 47 I-86100 Campobasso Italy 

出 版 物：《APPLIED THERMAL ENGINEERING》 (实用热力工程)

年 卷 期：2019年第163卷

页      面：114384-000页

核心收录：

学科分类：0820[工学-石油与天然气工程] 080702[工学-热能工程] 08[工学] 0807[工学-动力工程及工程热物理] 0802[工学-机械工程] 0801[工学-力学（可授工学、理学学位）] 

主　　题：Building energy optimization HVAC system Heating operation Weather-based control Multi-objective genetic algorithm Residential buildings 

摘      要：Many strategies are under investigation to reduce the environmental impact of the building stock. Among them, the implementation of optimal operation strategies of the HVAC (heating, ventilating and air conditioning) systems plays a fundamental role because it can produce substantial energy-economic savings and increment of thermal comfort. In this vein, a weather-data-based control framework is here proposed to provide optimal heating operation strategies easily applicable to a huge number of buildings. It works by coupling EnergyPlus and MATLAB (R) to run a multi-objective genetic algorithm and proposes a novel approach for multi-criteria decision-making. This latter addresses characteristic days (i.e., average cold days, average days and average hot days) of weather data files with the aim to provide monthly heating strategies that ensure the best compromise between running cost and thermal discomfort. As case studies, the proposed framework is applied to a residential building, representative of the Italian building stock from 1961 to 1975. In order to cover most of the Italian territory, four different cities are considered: Palermo (climatic zone B), Naples (C), Florence (D) and Milan (E). The achieved cost reduction is included between 6% (Milan) and 34% (Palermo), while the thermal comfort is not penalized. Finally, the framework provides practical indications ready to be easily applied to the Italian residential stock to achieve a significant and widespread improvement of energy performance.