Electricity price forecasting supports decision-making in energy markets and asset operation. Probabilistic forecasts are increasingly adopted to explicitly quantify uncertainty, typically issued as quantile predictions or ensembles of the full predictive distribution. However, how improvements in statistical forecast quality translate into economic value remains unclear. Battery storage arbitrage in day-ahead markets is a popular application-based benchmark for this purpose. We analyze quantile-based trading strategies (QBTS) and identify two critical flaws: they do not incentivize honest probabilistic forecasting and they ignore the intertemporal dependence structure of electricity prices. We therefore frame battery optimization as a stochastic program based on fully probabilistic forecasts and examine decision quality measurement for risk-neutral and risk-averse settings under different uncertainty models. Our discussion touches both sides of the coin: How reliable is the economic evaluation of forecasting models though (simplified) application studies - and how do improvements in statistical forecast quality for stochastic programs relate to the decision-quality and economic performance? We provide theoretical justification and empirical evidence from a case study on the German electricity market. Our results highlight the pitfalls of ranking forecasting models through battery trading strategies. We conclude with implications for evaluation practice and directions for future research in application-based forecast assessment.

翻译：电价预测为能源市场决策和资产运营提供支持。为显式量化不确定性，概率预测方法日益普及，通常以分位数预测或完整预测分布集成形式发布。然而，统计预测质量的提升如何转化为经济价值仍不明确。日前市场中的电池储能套利成为评估此问题的热门应用基准。本文分析了基于分位数的交易策略（QBTS），发现其存在两个关键缺陷：既未能激励诚实的概率预测，又忽略了电价的时间依赖结构。为此，我们将电池优化建模为基于完全概率预测的随机规划，研究不同不确定性模型下风险中性和风险规避决策的效用度量。本文同时探讨两方面问题：通过（简化）应用研究对预测模型进行经济评估的可靠性如何？随机规划中统计预测质量的改进如何影响决策质量与经济绩效？我们提供理论论证，并基于德国电力市场案例研究给出实证证据。结果揭示了通过电池交易策略对预测模型进行排序的潜在陷阱。最后总结了应用型预测评估实践的启示及未来研究方向。