Mori, Hiroyuki (Nakano Campus, Meiji University), Miwa, Rikuto (Meiji University)

A Sparse Model for Electricity Price Forecasting with LASSO-GRBFN and Brain Storm Optimization

Scheduled for presentation during the Invited Session "Optimal operation and control in smart grids" (ThuS2T2), Thursday, July 11, 2024, 13:10−13:30, Grand Amphitheater

12th IFAC Symposium on Control of Power & Energy Systems, July 10-12, 2024, Rabat, Morocco

This information is tentative and subject to change. Compiled on January 2, 2025

Abstract

This paper proposes a new method for predicting electricity prices. The method is based on the Generalized Radial Basis Function Network (GRBFN), an extension of the RBFN of Artificial Neural Network (ANN) used for one-step-ahead forecasting. The GRBFN is superior to the RBFN because it evaluates the Gaussian function parameters during learning. Conventional ANN methods consider overfitting with the weight decay method, based on the L2 norm of weights between neurons, but there is still room for improvement. Therefore, the paper proposes using the Least Absolute Shrinkage and Selection Operator (LASSO) to improve the model's performance.　Furthermore, the paper presents the Brain Storm Optimization (BSO) of evolutionary computation to evaluate the cost function with the term of the L1 norm. Conventional methods with the gradient are not suitable for the L1 norm. The proposed method is successfully applied to actual data of ISO New England in the USA.