Explainable Approaches for Forecasting Building Electricity Consumption

Nikos Sakkas (), Sofia Yfanti, Pooja Shah, Nikitas Sakkas, Christina Chaniotakis, Costas Daskalakis, Eduard Barbu and Marharyta Domnich
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Nikos Sakkas: Apintech Ltd., POLIS-21 Group, 4004 Limassol, Cyprus
Sofia Yfanti: Department of Mechanical Engineering, Hellenic Mediterranean University, 71410 Heraklion, Greece
Pooja Shah: Apintech Ltd., POLIS-21 Group, 4004 Limassol, Cyprus
Nikitas Sakkas: Apintech Ltd., POLIS-21 Group, 4004 Limassol, Cyprus
Christina Chaniotakis: Apintech Ltd., POLIS-21 Group, 4004 Limassol, Cyprus
Costas Daskalakis: Apintech Ltd., POLIS-21 Group, 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, 2023, vol. 16, issue 20, 1-20

Abstract: Building electric energy is characterized by a significant increase in its uses (e.g., vehicle charging), a rapidly declining cost of all related data collection, and a proliferation of smart grid concepts, including diverse and flexible electricity pricing schemes. Not surprisingly, an increased number of approaches have been proposed for its modeling and forecasting. In this work, we place our emphasis on three forecasting-related issues. First, we look at the forecasting explainability, that is, the ability to understand and explain to the user what shapes the forecast. To this extent, we rely on concepts and approaches that are inherently explainable, such as the evolutionary approach of genetic programming (GP) and its associated symbolic expressions, as well as the so-called SHAP (SHapley Additive eXplanations) values, which is a well-established model agnostic approach for explainability, especially in terms of feature importance. Second, we investigate the impact of the training timeframe on the forecasting accuracy; this is driven by the realization that fast training would allow for faster deployment of forecasting in real-life solutions. And third, we explore the concept of counterfactual analysis on actionable features, that is, features that the user can really act upon and which therefore present an inherent advantage when it comes to decision support. We have found that SHAP values can provide important insights into the model explainability. In our analysis, GP models demonstrated superior performance compared to neural network-based models (with a 20–30% reduction in Root Mean Square Error (RMSE)) and time series models (with a 20–40% lower RMSE), but a rather questionable potential to produce crisp and insightful symbolic expressions, allowing a better insight into the model performance. We have also found and reported here on an important potential, especially for practical, decision support, of counterfactuals built on actionable features, and short training timeframes.

Keywords: electricity demand forecasting; model explainability; SHAP values; neural networks; structured time series; genetic programming (GP); symbolic expressions; training timeframe; counterfactuals; actionable features (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: 2023
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