Forecasting the Occurrence of Electricity Price Spikes: A Statistical-Economic Investigation Study

Manuel Zamudio López, Hamidreza Zareipour () and Mike Quashie
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Manuel Zamudio López: Department of Electrical and Software Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
Hamidreza Zareipour: Department of Electrical and Software Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
Mike Quashie: Arcus Power, Calgary, AB T2P 3C5, Canada

Forecasting, 2024, vol. 6, issue 1, 1-23

Abstract: This research proposes an investigative experiment employing binary classification for short-term electricity price spike forecasting. Numerical definitions for price spikes are derived from economic and statistical thresholds. The predictive task employs two tree-based machine learning classifiers and a deterministic point forecaster; a statistical regression model. Hyperparameters for the tree-based classifiers are optimized for statistical performance based on recall, precision, and F1-score. The deterministic forecaster is adapted from the literature on electricity price forecasting for the classification task. Additionally, one tree-based model prioritizes interpretability, generating decision rules that are subsequently utilized to produce price spike forecasts. For all models, we evaluate the final statistical and economic predictive performance. The interpretable model is analyzed for the trade-off between performance and interpretability. Numerical results highlight the significance of complementing statistical performance with economic assessment in electricity price spike forecasting. All experiments utilize data from Alberta’s electricity market.

Keywords: electricity price forecasting; price spike occurrence forecasting; interpretable AI; forecast evaluation (search for similar items in EconPapers)
JEL-codes: A1 B4 C0 C1 C2 C3 C4 C5 C8 M0 Q2 Q3 Q4 (search for similar items in EconPapers)
Date: 2024
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