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Financial and energy performance analysis of efficiency measures in residential buildings. A probabilistic approach

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  • Scarpa, Federico
  • Tagliafico, Luca A.
  • Bianco, Vincenzo

Abstract

The present paper presents a methodology to effectively address the evaluation of building energy retrofitting projects in a highly uncertain context. Buildings are modelled in terms of archetypes which are characterized by specific features, e.g., U-values, heating plant typology, surface to volume ratio, etc. By using the Monte Carlo approach, the proposed method can address the influence of more than thirty important parameters on the final result in terms of energy savings, Net Present Value and other indices aimed to quantify the level of risk associated to complex energy efficiency interventions, e.g., energy saving at risk. The methodology is tested on a case study related to a building built in the ‘60s and located in Rome, Italy. However, the method is applicable irrespectively of the location, climatic conditions, and typology of the building. Results highlight that a retrofitting intervention consisting in wall insulation has a risk to be unprofitable equal to 47%. This can be ascribed to the mild climatic conditions of the location.

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  • Scarpa, Federico & Tagliafico, Luca A. & Bianco, Vincenzo, 2021. "Financial and energy performance analysis of efficiency measures in residential buildings. A probabilistic approach," Energy, Elsevier, vol. 236(C).
    • Handle: RePEc:eee:energy:v:236:y:2021:i:c:s0360544221017394
    DOI: 10.1016/j.energy.2021.121491
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    Cited by:

    1. Kilkis, Birol, 2022. "Net-zero buildings, what are they and what they should be?," Energy, Elsevier, vol. 256(C).
    2. Hettinga, Sanne & van ’t Veer, Rein & Boter, Jaap, 2023. "Large scale energy labelling with models: The EU TABULA model versus machine learning with open data," Energy, Elsevier, vol. 264(C).

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