Statistical Building Energy Model from Data Collection, Place-Based Assessment to Sustainable Scenarios for the City of Milan

Guglielmina Mutani (), Maryam Alehasin, Yasemin Usta, Francesco Fiermonte and Angelo Mariano
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Guglielmina Mutani: Department of Energy, Politecnico di Torino, 10129 Turin, Italy
Maryam Alehasin: Department of Energy, Politecnico di Torino, 10129 Turin, Italy
Yasemin Usta: Department of Energy, Politecnico di Torino, 10129 Turin, Italy
Francesco Fiermonte: Urban Sustainability & Security Laboratory for Social Challenges, S3+Lab, Politecnico di Torino, 10125 Turin, Italy
Angelo Mariano: Energy Technologies and Renewable Sources Department, ICT Division, ENEA (Agenzia Nazionale per le Nuove Tecnologie, L’energia e lo Sviluppo Economico Sostenibile—National Agency for New Technologies, Energy and Sustainable Economic Development), 70125 Bari, Italy

Sustainability, 2023, vol. 15, issue 20, 1-36

Abstract: Building energy modeling plays an important role in analyzing the energy efficiency of the existing building stock, helping in enhancing it by testing possible retrofit scenarios. This work presents an urban scale and place-based approach that utilizes energy performance certificates to develop a statistical energy model. The objective is to describe the energy modeling methodology for evaluating the energy performance of residential buildings in Milan; in addition, a comprehensive reference dataset for input data from available open databases in Italy is provided—a critical step in assessing energy consumption and production at territorial scale. The study employs open-source software QGIS 3.28.8 to model and calculate various energy-related variables for the prediction of space heating, domestic hot water consumptions, and potential solar production. By analyzing demand/supply profiles, the research aims to increase energy self-consumption and self-sufficiency in the urban context using solar technologies. The presented methodology is validated by comparing simulation results with measured data, achieving a Mean Absolute Percentage Error (MAPE) of 5.2%, which is acceptable, especially considering city-scale modeling. The analysis sheds light on key parameters affecting building energy consumption/production, such as type of user, volume, surface-to-volume ratio, construction period, systems’ efficiency, solar exposition and roof area. Additionally, this assessment attempts to evaluate the spatial distribution of energy-use and production within urban environments, contributing to the planning and realization of smart cities.

Keywords: Urban Building Energy Modeling; statistical model; urban scale; residential buildings; energy performance certificates (EPCs); energy efficiency; renewable energy sources; solar technologies; space heating; domestic hot water; electrical consumption; QGIS; self-sufficiency; self-consumption (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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