Weather-data-based control of space heating operation via multi-objective optimization: Application to Italian residential buildings. (25th December 2019)

• Record Type: Journal Article
• Authors: Ascione, Fabrizio; Bianco, Nicola; Mauro, Gerardo Maria; Napolitano, Davide Ferdinando; Vanoli, Giuseppe Peter
• Is Part Of: Applied thermal engineering. Volume 163(2019)
• Publication Date: 2019-12-25
• Subjects: Building energy optimization -- HVAC system -- Heating operation -- Weather-based control -- Multi-objective genetic algorithm -- Residential buildings; Heat engineering -- Periodicals; Heating -- Equipment and supplies -- Periodicals; Periodicals; 621.40205
• DOI: 10.1016/j.applthermaleng.2019.114384
• Languages: English
• ISSNs: 1359-4311

Abstract

Graphical abstract: Highlights: Multi-objective genetic algorithm is used for weather-data-based control. Heating operation is optimized by minimizing running cost and thermal discomfort. A new multi-criteria decision making is performed considering characteristic days. Italian residential buildings are examined by considering different climatic zones. High energy cost savings are attained, the results can be applied on large-scale. Abstract: 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® 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.