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Predicting Building Energy Demand and Retrofit Potentials Using New Climatic Stress Indices and Curves

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  • Rosa Francesca De Masi

    (DING-Department of Engineering, University of Sannio, Piazza Roma, 21, 82100 Benevento, Italy)

  • Gerardo Maria Mauro

    (DING-Department of Engineering, University of Sannio, Piazza Roma, 21, 82100 Benevento, Italy)

  • Silvia Ruggiero

    (DING-Department of Engineering, University of Sannio, Piazza Roma, 21, 82100 Benevento, Italy)

  • Francesca Villano

    (DING-Department of Engineering, University of Sannio, Piazza Roma, 21, 82100 Benevento, Italy)

Abstract

Building energy requalification in Italy and Europe has been much discussed in recent years due to the high percentage of existing buildings with poor energy performance. In this context, it is useful to obtain a user-friendly and fast tool to predict the thermal energy demand ( TED ) for space conditioning and the related primary energy consumption ( PEC ) as a function of climatic stress. In this study, the SLABE methodology (simulation-based large-scale uncertainty/sensitivity analysis of building energy performance) is used to simulate representative Italian buildings, varying parameters such as geometry, envelope and HVAC (heating, ventilating and space conditioning) systems. MATLAB ® in combination with EnergyPlus is used to analyze 200 buildings belonging to two structural types (multi-family buildings and apartment blocks) built in 1961–1975. Nine scenarios (as-built scenarios and eight retrofit ones) are investigated in 63 climatic locations. A regression analysis shows that the classical HDDs (heating degree days) approach cannot give an accurate prediction of TED because solar radiation is not accounted for. Thus, new climatic indices are developed alongside solar radiation, including the heating stress index ( HSI ), the cooling stress index ( CSI ) and the yearly climatic stress index ( YCSI ). The purpose of our work is to obtain climatic stress curves for the prediction of TED and PEC . Testing of this novel approach is performed by comparison with another building energy simulation tool, showing a low discrepancy, i.e., the coefficient of variation of the root mean square error is between 12% and 28%, which confirms certain reliability of the approach here proposed.

Suggested Citation

  • Rosa Francesca De Masi & Gerardo Maria Mauro & Silvia Ruggiero & Francesca Villano, 2023. "Predicting Building Energy Demand and Retrofit Potentials Using New Climatic Stress Indices and Curves," Energies, MDPI, vol. 16(16), pages 1-23, August.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:16:p:5861-:d:1212571
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    References listed on IDEAS

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