Optimizing Energy Storage Profits: A New Metric for Evaluating Price Forecasting Models

Simone Sbaraglia, Alessandro Fiori Maccioni () and Stefano Zedda
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Simone Sbaraglia: Department of Economics and Business Sciences, University of Cagliari, 09123 Cagliari, Italy
Alessandro Fiori Maccioni: Department of Economics and Business Sciences, University of Cagliari, 09123 Cagliari, Italy
Stefano Zedda: Department of Economics and Business Sciences, University of Cagliari, 09123 Cagliari, Italy

JRFM, 2024, vol. 17, issue 12, 1-29

Abstract: Storage profit maximization is based on buying energy at the lowest prices and selling it at the highest prices. The best strategy must thus be based on both accurately predicting the price peak hours and on rightly choosing when to buy and when to sell the stored energy. In this aim, price prediction is crucial, but choosing the prediction model by means of the usual metrics, as the lowest mean squared error, is not an effective solution as the mean squared error computation equally weights the prediction error of all prices, while the focus must be on the higher and lower prices. In this paper, we propose a new metric focused on the correct forecasting of high and low prices so as to allow for a more effective choice among price forecasting models. Results show that the new metric outperforms the standard metrics, allowing for a more accurate estimation of the possible profit for storage (or other trading) activities.

Keywords: electricity markets; price forecasting; forecasting accuracy metrics; energy storage (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2024
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