An Improved and Explainable Electricity Price Forecasting Model via SHAP-Based Error Compensation Approach

Abstract

Forecasting errors in power markets, even as small as 1%, can have significant financial implications. However, even high-performance artificial intelligence (AI) based electricity price forecasting (EPF) models have instances when their prediction error is much higher than those shown by mean performance metrics. To date, explainable AI has been used to enhance the model transparency and trustworthiness of AI-based EPF models. However, this article demonstrates that insights from explainable AI (XAI) techniques can be expanded beyond its primary task of explanatory visualizations. This work presents a XAI-based error compensation approach to improve model performance and identify irregular predictions. The first phase of the proposed approach involves error quantification through a Shapley additive explanations (SHAP) based corrector model that fine-tunes the base predictor's forecasts. Using this corrector model's SHAP explanations, the proposed approach distinguishes high-accuracy predictions from lower ones in the second stage. Additionally, these explanations are more simplified than the base model, making them easier for nonexpert users such as bidding agents. Performance enhancement and insightful user-centric explanations are crucial for real-world scenarios such as price spikes during network congestion, high renewable penetration, and fluctuating fuel costs. Case studies discussed here show the efficacy of the proposed approach independent of model architecture, feature combination, or behavioral patterns of electricity prices in different markets.