  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. The 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 (ML) based predictions could
provide an efficient tool for LMP prediction, especially in energy markets with
intermittent sources like renewable energy. This 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 ML models can predict LMP 4-5 orders of magnitude faster than traditional
OPF solvers with 5-6\% error rate, highlighting the potential of ML models in
LMP prediction for large-scale power models with the assistance of hardware
infrastructure like multi-core CPUs and GPUs in modern HPC clusters.
