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Interpretable Forecasting of Energy Demand in the Residential Sector

Author

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  • Nikos Sakkas

    (Department of Mechanical Engineering, Hellenic Mediterranean University, 71410 Heraklion, Greece)

  • Sofia Yfanti

    (Department of Mechanical Engineering, Hellenic Mediterranean University, 71410 Heraklion, Greece)

  • Costas Daskalakis

    (Apintech Ltd., POLIS-21 Group, Spatharikou 5 Str., 4004 Limassol, Cyprus)

  • Eduard Barbu

    (Institute of Computer Science, University of Tartu, Narva mnt 18, 51009 Tartu, Estonia)

  • Marharyta Domnich

    (Institute of Computer Science, University of Tartu, Narva mnt 18, 51009 Tartu, Estonia)

Abstract

Energy demand forecasting is practiced in several time frames; different explanatory variables are used in each case to serve different decision support mandates. For example, in the short, daily, term building level, forecasting may serve as a performance baseline. On the other end, we have long-term, policy-oriented forecasting exercises. TIMES (an acronym for The Integrated Markal Efom System) allows us to model supply and anticipated technology shifts over a long-term horizon, often extending as far away in time as 2100. Between these two time frames, we also have a mid-term forecasting time frame, that of a few years ahead. Investigations here are aimed at policy support, although in a more mid-term horizon, we address issues such as investment planning and pricing. In this paper, we develop and evaluate statistical and neural network approaches for this mid-term forecasting of final energy and electricity for the residential sector in six EU countries (Germany, the Netherlands, Sweden, Spain, Portugal and Greece). Various possible approaches to model the explanatory variables used are presented, discussed, and assessed as to their suitability. Our end goal extends beyond model accuracy; we also include interpretability and counterfactual concepts and analysis, aiming at the development of a modelling approach that can provide decision support for strategies aimed at influencing energy demand.

Suggested Citation

  • Nikos Sakkas & Sofia Yfanti & Costas Daskalakis & Eduard Barbu & Marharyta Domnich, 2021. "Interpretable Forecasting of Energy Demand in the Residential Sector," Energies, MDPI, vol. 14(20), pages 1-17, October.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:20:p:6568-:d:654676
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    References listed on IDEAS

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    Cited by:

    1. Aras, Serkan & Hanifi Van, M., 2022. "An interpretable forecasting framework for energy consumption and CO2 emissions," Applied Energy, Elsevier, vol. 328(C).

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