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Building Stock Energy Model: Towards a Stochastic Approach

Author

Listed:
  • Marta J. N. Oliveira Panão

    (Instituto Dom Luiz (IDL), Faculdade de Ciências da Universidade de Lisboa, Campo Grande, 1749-016 Lisboa, Portugal)

  • André Penas

    (Becquerel Institute, 1000 Brussels, Belgium)

Abstract

This work uses the outcome of a computational tool that performs Energy Performance Certification (EPC) data processing and transforms raw data into comparable data. Multi-correlation among variables results in probability distributions for the most relevant form and fabric building parameters. The model consistently predicts the distributions for heating and cooling energy needs for the Lisbon Metropolitan Area, with an error below 7% for the first, second and third quartiles. Differences in the energy needs estimation are below 6% when comparing the seasonal steady-state with the resistance-capacitance (RC) model, which proved to be a robust alternative algorithm capable of modeling hourly user profiles. The RC model calculates electricity consumption for actual, adequate, and minimum thermal comfort scenarios corresponding to different user profiles. The actual scenario, built from statistics and a previous survey, defines a reference to evaluate other scenarios for the mean electricity consumption for space heating and cooling in the building units with those systems. The results show that the actual mean electricity consumption for heating (610 kWh/y) is slightly above the minimum (512 kWh/y), with 37% of building units potentially under heated. The electricity consumption (108 kWh/y) for cooling is below the minimum (129 kWh/y).

Suggested Citation

  • Marta J. N. Oliveira Panão & André Penas, 2022. "Building Stock Energy Model: Towards a Stochastic Approach," Energies, MDPI, vol. 15(4), pages 1-21, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1420-:d:750317
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    References listed on IDEAS

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    1. Swan, Lukas G. & Ugursal, V. Ismet, 2009. "Modeling of end-use energy consumption in the residential sector: A review of modeling techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 1819-1835, October.
    2. Dall’O’, Giuliano & Sarto, Luca & Sanna, Nicola & Tonetti, Valeria & Ventura, Martina, 2015. "On the use of an energy certification database to create indicators for energy planning purposes: Application in northern Italy," Energy Policy, Elsevier, vol. 85(C), pages 207-217.
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    Cited by:

    1. Piotr Michalak, 2022. "Thermal Network Model for an Assessment of Summer Indoor Comfort in a Naturally Ventilated Residential Building," Energies, MDPI, vol. 15(10), pages 1-19, May.

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