Contents lists available at ScienceDirect Applied Thermal Engineering journal homepage: www.elsevier.com/locate/apthermeng Weather-data-based control of space heating operation via multi-objective optimization: Application to Italian residential buildings Fabrizio Ascione a , Nicola Bianco a , Gerardo Maria Mauro b, ⁎ , Davide Ferdinando Napolitano c , Giuseppe Peter Vanoli d a Università degli Studi di Napoli Federico II, Department of Industrial Engineering, Piazzale Tecchio 80, 80125 Naples, Italy b Università degli Studi del Sannio, Department of Engineering, Piazza Roma 21, 82100 Benevento, Italy c Università degli Studi di Bergamo, Via Salvecchio, 19, 24129 Bergamo, Italy d Università degli studi del Molise, Department of Medicine, Via Cesare Gazzani 47, 86100 Campobasso, Italy 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 character- istic days. • Italian residential buildings are ex- amined by considering different cli- matic zones. • High energy cost savings are attained, the results can be applied on large- scale. GRAPHICAL ABSTRACT ARTICLE INFO Keywords: Building energy optimization HVAC system Heating operation Weather-based control Multi-objective genetic algorithm Residential buildings 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 de- cision-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. https://doi.org/10.1016/j.applthermaleng.2019.114384 Received 28 November 2018; Received in revised form 6 September 2019; Accepted 12 September 2019 ⁎ Corresponding author. E-mail addresses: fabrizio.ascione@unina.it (F. Ascione), nicola.bianco@unina.it (N. Bianco), germauro@unisannio.it (G.M. Mauro), davideferdinando.napolitano@unibg.it (D.F. Napolitano), giuseppe.vanoli@unimol.it (G.P. Vanoli). Applied Thermal Engineering 163 (2019) 114384 Available online 14 September 2019 1359-4311/ © 2019 Elsevier Ltd. All rights reserved. T