Explainability-based Trust Algorithm for electricity price forecasting models - Leena Heistrene; Ram Machlev; Michael Perl; Juri Belikov; Dmitry Baimel; Kfir Levy; Shie Mannor; Yoash Levron (Energy and AI, 2023)
Advanced machine learning (ML) algorithms have outperformed traditional approaches in various forecasting applications, especially electricity price forecasting (EPF). However, the prediction accuracy of ML reduces substantially if the input data is not similar to the ones seen by the model during training. This is often observed in EPF problems when market dynamics change owing to a rise in fuel prices, an increase in renewable penetration, a change in operational policies, etc. While the dip in model accuracy for unseen data is a cause for concern, what is more, challenging is not knowing when the ML model would respond in such a manner. Such uncertainty makes the power market participants, like bidding agents and retailers, vulnerable to substantial financial loss caused by the prediction errors of EPF models. Therefore, it becomes essential to identify whether or not the model prediction at a given instance is trustworthy. In this light, this paper proposes a trust algorithm for EPF users based on explainable artificial intelligence techniques. The suggested algorithm generates trust scores that reflect the model’s prediction quality for each new input. These scores are formulated in two stages: in the first stage, the coarse version of the score is formed using correlations of local and global explanations, and in the second stage, the score is fine-tuned further by the Shapley additive explanations values of different features. Such score-based explanations are more straightforward than feature-based visual explanations for EPF users like asset managers and traders. A dataset from Italy’s and ERCOT’s electricity market validates the efficacy of the proposed algorithm. Results show that the algorithm has more than 85% accuracy in identifying good predictions when the data distribution is similar to the training dataset. In the case of distribution shift, the algorithm shows the same accuracy level in identifying bad predictions.