Statistical and economic evaluation of forecasts in electricity markets: beyond RMSE and MAE

TL;DR

This paper explores how different statistical properties of electricity price forecasts impact their economic value in BESS arbitrage, highlighting the limitations of traditional accuracy metrics.

Contribution

It introduces alternative forecast evaluation measures that better predict economic performance, moving beyond RMSE and MAE.

Findings

Traditional accuracy metrics are weakly correlated with BESS profits.

Dispersion and association measures better reflect forecast economic value.

Incorporating new evaluation criteria can improve forecast selection for operational decisions.

Abstract

Electricity price forecasts are typically evaluated using accuracy measures such as RMSE and MAE, although these metrics often fail to reflect their economic value in operational decisions. This paper investigates which statistical properties of electricity price forecasts are most relevant for economic performance, using battery energy storage system (BESS) arbitrage as an application. We assess prediction quality along four dimensions: forecast accuracy, intraday error dispersion, association between predicted and realized prices, and the ability to identify daily price extrema. We construct a comprehensive pool of 192 hourly day-ahead electricity price forecasts and use it to evaluate the relationship between proposed quality measures and profits generated for two representative BESS configurations. The results show that traditional accuracy metrics are only weakly correlated with BESS income. At the same time, dispersion- and association-based measures better capture a forecast's economic value by reflecting its ability to reproduce daily price patterns. These findings demonstrate that incorporating complementary evaluation criteria may improve forecast selection and enhance the economic performance of BESS.