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

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.