Accurate price predictions are essential for market participants in order to optimize their operational schedules and bidding strategies, especially in the current context where electricity prices become more volatile and less predictable using classical approaches. Locational Marginal Pricing (LMP) pricing mechanism is used in many modern power markets, where the traditional approach utilizes optimal power flow (OPF) solvers. However, for large electricity grids this process becomes prohibitively time-consuming and computationally intensive. Machine learning solutions could provide an efficient tool for LMP prediction, especially in energy markets with intermittent sources like renewable energy. The study evaluates the performance of popular machine learning and deep learning models in predicting LMP on multiple electricity grids. The accuracy and robustness of these models in predicting LMP is assessed considering multiple scenarios. The results show that machine learning models can predict LMP 4-5 orders of magnitude faster than traditional OPF solvers with 5-6\% error rate, highlighting the potential of machine learning models in LMP prediction for large-scale power models with the help of hardware solutions like multi-core CPUs and GPUs in modern HPC clusters.

翻译：准确的电价预测对于市场参与者优化运营计划与竞价策略至关重要，特别是在当前电价波动加剧、传统方法预测准确性下降的背景下。节点边际电价（LMP）定价机制被广泛应用于现代电力市场，传统方法依赖最优潮流（OPF）求解器。然而，对于大型电网而言，该过程耗时过长且计算强度大。机器学习方法可为LMP预测提供高效工具，尤其适用于含可再生能源等间歇性能源的电力市场。本研究评估了主流机器学习与深度学习模型在多个电网中预测LMP的性能，并针对多种场景检验了这些模型的预测准确性与鲁棒性。结果表明，机器学习模型预测LMP的速度比传统OPF求解器快4-5个数量级，误差率仅为5-6%，凸显了机器学习模型借助现代高性能计算集群中的多核CPU和GPU等硬件解决方案，在大规模电力模型LMP预测中的潜力。