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A GIS-Based Procedure for Estimating the Energy Demand Profiles of Buildings towards Urban Energy Policies

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  • Simone Ferrari

    (Department of Architecture, Built Environment and Construction Engineering (ABC), Politecnico di Milano, 20133 Milano, Italy)

  • Federica Zagarella

    (Department of Architecture, Built Environment and Construction Engineering (ABC), Politecnico di Milano, 20133 Milano, Italy)

  • Paola Caputo

    (Department of Architecture, Built Environment and Construction Engineering (ABC), Politecnico di Milano, 20133 Milano, Italy)

  • Giuliano Dall’O’

    (Department of Architecture, Built Environment and Construction Engineering (ABC), Politecnico di Milano, 20133 Milano, Italy)

Abstract

Assessing the existing building stock’s hourly energy demand and predicting its variation due to energy efficiency measures are fundamental for planning strategies towards renewable-based Smart Energy Systems. However, the need for accurate methods for this purpose in the literature arises. The present article describes a GIS-based procedure developed for estimating the energy demand profiles of urban buildings based on the definition of the volumetric consistency of a building stock, characterized by different ages of construction and the most widespread uses, as well as dynamic simulations of a set of Building Energy Models adopting different energy-related features. The simulation models are based on a simple Building Energy Concept where selected thermal zones, representative of different boundary conditions options, are accounted. By associating the simulated hourly energy density profiles to the geo-referenced building stock and to the surveyed thermal system types, the whole hourly energy profile is estimated for the considered area. The method was tested on the building stock of Milan (Italy) and validated with the data available from the annual energy balance of the city. This procedure could support energy planners in defining urban energy demand profiles for energy policy scenarios.

Suggested Citation

  • Simone Ferrari & Federica Zagarella & Paola Caputo & Giuliano Dall’O’, 2021. "A GIS-Based Procedure for Estimating the Energy Demand Profiles of Buildings towards Urban Energy Policies," Energies, MDPI, vol. 14(17), pages 1-16, September.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:17:p:5445-:d:627078
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    References listed on IDEAS

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    Cited by:

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    2. Tamás Storcz & Géza Várady & István Kistelegdi & Zsolt Ercsey, 2023. "Regression Models and Shape Descriptors for Building Energy Demand and Comfort Estimation," Energies, MDPI, vol. 16(16), pages 1-20, August.
    3. Simone Ferrari & Federica Zagarella & Paola Caputo & Marco Beccali, 2023. "Mapping Seasonal Variability of Buildings Electricity Demand profiles in Mediterranean Small Islands," Energies, MDPI, vol. 16(4), pages 1-16, February.
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    5. Alberto Barbaresi & Mattia Ceccarelli & Giulia Menichetti & Daniele Torreggiani & Patrizia Tassinari & Marco Bovo, 2022. "Application of Machine Learning Models for Fast and Accurate Predictions of Building Energy Need," Energies, MDPI, vol. 15(4), pages 1-16, February.

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