Forecasting electricity prices is a challenging task and an active area of research since the 1990s and the deregulation of the traditionally monopolistic and government-controlled power sectors. Although it aims at predicting both spot and forward prices, the vast majority of research is focused on short-term horizons which exhibit dynamics unlike in any other market. The reason is that power system stability calls for a constant balance between production and consumption, while being weather (both demand and supply) and business activity (demand only) dependent. The recent market innovations do not help in this respect. The rapid expansion of intermittent renewable energy sources is not offset by the costly increase of electricity storage capacities and modernization of the grid infrastructure. On the methodological side, this leads to three visible trends in electricity price forecasting research as of 2022. Firstly, there is a slow, but more noticeable with every year, tendency to consider not only point but also probabilistic (interval, density) or even path (also called ensemble) forecasts. Secondly, there is a clear shift from the relatively parsimonious econometric (or statistical) models towards more complex and harder to comprehend, but more versatile and eventually more accurate statistical/machine learning approaches. Thirdly, statistical error measures are nowadays regarded as only the first evaluation step. Since they may not necessarily reflect the economic value of reducing prediction errors, more and more often, they are complemented by case studies comparing profits from scheduling or trading strategies based on price forecasts obtained from different models.

翻译：预测电力价格是一项具有挑战性的任务,也是自1990年代以来对传统垄断和政府控制的电力部门放松管制的研究领域。尽管研究的绝大多数着眼于预测现价和远期价格,但研究的重点放在短期前景上,这些前景与其他市场不同,其原因是电力系统的稳定要求生产和消费之间保持持续平衡,同时取决于天气(供求)和商业活动(仅需求),最近的市场创新在这方面没有帮助。断断续续续的可再生能源来源的迅速扩展并没有被电力储存能力和电网基础设施现代化的昂贵增长所抵消。在方法方面,这导致2022年电力价格预测研究的三个明显趋势。第一,缓慢但每年更加明显,不仅考虑点,而且考虑概率(交错、密度)甚至路径(也称为“通俗”预测。第二,从相对偏差的生态计量(或统计)模型向更复杂和困难的理解,但更难于比较的预测,因此,统计/机器的预测方法往往不能反映更准确的预测,因为统计/机器的预测方法可能更能反映更准确地反映现在的预测结果。