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. Specifically, we leverage functional principal component analysis to efficiently represent a pair of supply and demand curves in a low-dimensional vector space and employ regularized vector autoregressive models for their prediction. 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，并从中推导出点预测和概率性电价预测。具体而言，我们利用函数主成分分析，将一对供需曲线高效地表示在低维向量空间中，并采用正则化向量自回归模型进行预测。我们使用2023-2024年意大利日前市场的数据，对所提出的基于曲线的模型（即从预测的优先次序曲线推导得出）与直接预测出清电价的最先进基于价格的模型，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显著提高了点预测和概率性电价预测的准确性，平均增益约为5%，且在中午时段（此时由于可再生能源发电量高和需求低，电价偶尔会下降）改进高达10%。