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.

 该研究评估了在多个电力网格上使用流行的机器学习和深度学习模型预测LMP的性能，表明机器学习模型相比传统的最优功率流求解器在LMP预测方面具有更快的速度和更低的误差率，这为大规模电力模型中的LMP预测提供了潜力。

基于人工智能的近实时边际定价方法：可行性和鲁棒性研究