A Comparative Analysis of Price Forecasting Methods for Maximizing Battery Storage Profits

Alessandro Fiori Maccioni (), Simone Sbaraglia, Rahim Mahmoudvand and Stefano Zedda
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Alessandro Fiori Maccioni: Department of Economic and Business Sciences, University of Cagliari, Via Sant’Ignazio 74, 09123 Cagliari, Italy
Simone Sbaraglia: Department of Economic and Business Sciences, University of Cagliari, Via Sant’Ignazio 74, 09123 Cagliari, Italy
Rahim Mahmoudvand: Department of Economic and Business Sciences, University of Cagliari, Via Sant’Ignazio 74, 09123 Cagliari, Italy
Stefano Zedda: Department of Economic and Business Sciences, University of Cagliari, Via Sant’Ignazio 74, 09123 Cagliari, Italy

Energies, 2025, vol. 18, issue 13, 1-31

Abstract: Battery energy storage systems (BESS) rely on accurate electricity price forecasts to maximize arbitrage profits in day-ahead markets. We examined whether specific forecasting models, ranging from statistical benchmarks to machine learning methods, consistently deliver superior financial outcomes for storage operators. Using real market data from the Italian day-ahead electricity market over 2020–2024, we compared univariate singular spectrum analysis (SSA), ARIMA, SARIMA, random forests, and a 30-day simple moving average under a unified trading framework. All models were evaluated based on their ability to generate arbitrage profits. Univariate SSA clearly outperformed all alternatives, achieving on average 98% of the theoretical maximum profit while maintaining the lowest forecast error. Among the other models, simpler approaches performed surprisingly well: they achieved comparable, if not superior, profit performance to more complex, hour-specific, or computationally intensive configurations. These results were robust to plausible variations in battery parameters and retraining schedules, suggesting that univariate SSA offers a uniquely effective forecasting solution for battery arbitrage and that simplicity can often be more effective than complexity in operational revenue terms.

Keywords: day-ahead electricity markets; battery arbitrage; electricity price forecasting; singular spectrum analysis; ARIMA; SARIMA; random forest (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: 2025
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