AI 中文总结
该研究针对电价多维概率预测，提出带平均策略的MS方法（MS-ave），经德、西2021-2024年日前市场数据验证，其在预测指标及BESS交易策略支持上均优于其他方法，可支撑BESS的充放电决策与风险管理。
AI 中文摘要
本文研究了多种构建电价多维概率预测的方法，在多重拆分（Multiple Split, MS）方法的基础上，融入了不同长度估计窗口的预测平均策略，并将其性能与其他成熟方法进行对比。研究表明，电价分布的集成表示在电池储能系统（BESS）管理中尤为有用，它能直接构建每日利润的概率预测，这些预测可用于确定最优充放电时段，并支持风险管理决策。研究使用2021-2024年德国和西班牙日前电力市场的数据对方法进行评估，结果显示，带平均策略的MS方法（MS-ave）在预测区间覆盖概率（PICP）、连续等级概率评分（CRPS）和能量评分（ES）方面，总体优于其他所考虑的方法；此外，该方法在支持BESS交易策略方面表现更优，尤其是在存在非零运营成本的情况下。
英文摘要
This article examines various methods of constructingmultidimensional probabilistic forecasts of electricity prices. Building on the Multiple Split (MS) method, it incorporates forecast averaging across estimation windows of different lengths and compares its performance with that of other, well-established methods. The research demonstrates that the ensemble representation of the price distribution is particularly useful in battery energy storage system (BESS) management. It enables the direct construction of probabilistic forecasts of daily profits. These forecasts can be used to determine optimal charging and discharging hours, as well as to support risk management decisions. The methods are evaluated using data from the German and Spanish day-ahead electricity markets from 2021-2024. The results indicate that theMS method with averaging (MS-ave) generally outperforms the other considered approaches in terms of Prediction Interval Coverage Probability (PICP), the Continuous Ranked Probability Score (CRPS) and the Energy Score (ES). Moreover, it is superior in supporting BESS trading strategies, particularly in case of non-zero operational costs.
