Self-Supervised Adaptive Learning Algorithm for Multi-Horizon Electricity Price Forecasting

Abstract

Forecasting accuracy of electricity prices is crucial to the optimal operation of the electricity market, as improper forecasting can lead to inefficiencies, increased costs, and market instability. Thus, it is highly desired to develop a robust electricity price forecasting framework. The development of an optimal forecasting model depends on the proper choice of exogenous variables, and as the impact/characteristics of the input variables may change over time, thus the choice of appropriate external variables should be a dynamic task. Therefore, it is necessary to develop an online adaptive forecasting model, which will not only continuously forecast but also learn automatically by sensing the changes in the relationship of the variables. To sense the changes and to develop a parsimonious model proper feature engineering is required. Multi-level correlation with multicollinearity has been considered as the feature engineering tool for online training to create an accurate forecasting model. After analyzing existing studies and analyzing the gaps, an approach is proposed, utilizing a General Regression Neural Network (GRNN) with advanced feature engineering and simultaneous adaptive learning, that can outperform traditional models like ANN, RNN, and LSTM in terms of forecasting accuracy.

Keywords

• Electricity price forecasting
• online adaptive learning
• maximal information coefficient
• general regression neural network
• long short-term memory
• recurrent neural network