Interpretable Forecasting of Energy Demand in the Residential Sector

Nikos Sakkas, Sofia Yfanti, Costas Daskalakis, Eduard Barbu and Marharyta Domnich
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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

Energies, 2021, vol. 14, issue 20, 1-17

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.

Keywords: residential energy demand forecasting; interpretability; counterfactuals; decision support (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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