We introduce a general, simple, and computationally efficient framework for predicting day-ahead supply and demand merit-order curves, from which both point and probabilistic electricity price forecasts can be derived. We conduct a rigorous empirical comparison of price forecasting performance between the proposed curve-based model, i.e., derived from predicted merit-order curves, and state-of-the-art price-based models that directly forecast the clearing price, using data from the Italian day-ahead market over the 2023-2024 period. Our results show that the proposed curve-based approach significantly improves both point and probabilistic price forecasting accuracy relative to price-based approaches, with average gains of approximately 5%, and improvements of up to 10% during mid-day hours, when prices occasionally drop due to high renewable generation and low demand.

翻译：本文提出了一种通用、简单且计算高效的框架，用于预测日前供需优先次序曲线，从中可推导出电价的点预测和概率预测。我们使用意大利日前市场2023-2024年期间的数据，对基于曲线（即通过预测优先次序曲线推导得出）的模型与直接预测出清电价的最先进基于价格模型，进行了严格的电价预测性能实证比较。结果表明，相较于基于价格的方法，所提出的基于曲线的方法在点预测和概率预测精度上均有显著提升，平均增益约为5%，且在正午时段（此时高可再生能源发电量和低需求偶尔导致电价下跌）的改进幅度最高可达10%。