P23 - Evaluation of GPU Accelerated Machine Learning Algorithms for Energy Price Prediction
DescriptionThe Locational Marginal Pricing (LMP) mechanism is a way to calculate the cost of providing electricity to a specific point in the grid. Accurate forecasting of LMP is important for market participants such as power producers or financial institutions to optimize operations and bidding strategies. The LMP is calculated using the optimal power flow (OPF) problem, which is a constrained nonlinear optimization problem to determine the least-cost power generation in the grid. However, this can be a time-consuming and computationally demanding task. Recent efforts have focused on using machine learning techniques, such as Decision Tree Regressor, Random Forest Regressor, Gradient Boosting Regressor, and Deep Neural Networks, to predict LMP more efficiently. Modern machine learning libraries like Scikit-Learn and PyTorch are optimised to use multi-core CPU and GPU architectures that are common in modern High-Performance Computing (HPC) clusters. These models have been tested on multiple electricity grids and found to be 4-5 orders of magnitude faster than traditional methods. However, they do have slightly higher error rates on edge-case scenarios. Overall, there is a strong case for using machine learning models for LMP prediction on large scale electricity grids with the aid of HPC resources.
TimeMonday, June 2611:20 - 11:50 CEST