TY - JOUR
T1 - Explainability-Driven Trust Framework for Electricity Price Forecasting Models
AU - Kevin O’Brien
AU - Liam Murphy
AU - Sean Doyle
JF - Journal of Smart Grids, Energy Analytics and Optimization
JO - J Smart Grids Energy Anal Optim
Y1 - 2025
VL - 4
IS - 1
SP - 101
N2 - Sophisticated machine learning (ML) techniques have demonstrated superior performance over conventional methods across numerous forecasting tasks, particularly in electricity price forecasting (EPF). Nevertheless, the predictive capability of these ML models deteriorates markedly when encountering input data that differs significantly from the examples observed during training. This phenomenon frequently occurs in EPF due to shifts in market conditions, such as surges in fuel costs, higher integration of renewable energy sources, or modifications in regulatory frameworks and operational rules. While the decline in accuracy on unfamiliar data is problematic, an even greater difficulty lies in the inability to anticipate when the model will produce unreliable outputs. This lack of foresight exposes electricity market participants—including bidding agents and retailers—to considerable financial risks stemming from erroneous price predictions. Consequently, it is critical to determine whether a given model prediction can be relied upon in real time. To address this need, the present study introduces a trust evaluation algorithm for EPF applications grounded in explainable artificial intelligence (XAI) methods. The proposed algorithm computes trust scores that indicate the reliability of each new prediction generated by the model. These scores are constructed in two phases: first, a preliminary score is derived from the correlation between local and global explanations; second, the score is refined using SHapley Additive exPlanations (SHAP) values for individual features. Such score-based interpretations are more accessible and actionable for EPF practitioners, such as asset managers and traders, than conventional feature-based visualizations. The effectiveness of the algorithm is assessed using datasets from the Italian electricity market and the Electric Reliability Council of Texas (ERCOT). Empirical results reveal that the method achieves over 85% accuracy in recognizing reliable predictions when the incoming data distribution closely matches the training distribution. Under conditions of distributional shift, the algorithm maintains a comparable level of accuracy in correctly flagging unreliable predictions.
UR - https://isespub.com/k127483564
ER -