Explainable AI for Electricity Price Anomaly Detection: A SHAP-Driven Approach in Romania’s Energy Market

This research presents an approach to anomaly detection in electricity price data using explainable artificial intelligence (XAI) techniques. The study focuses on the Romanian electricity market, analyzing hourly price data alongside generation and load variables to identify and explain price anomalies. We use Isolation Forest for anomaly detection and Random Forest for predictive modeling, while SHAP (SHapley Additive exPlanations) values provide interpretability of the detected anomalies. Our methodology categorizes anomalies into price spikes, price drops, and other anomalies, revealing distinct patterns in each category. Results show that renewable energy generation, particularly wind and solar, significantly influences price drops, while load forecast deviations and conventional generation constraints contribute to price spikes. This framework offers insights for market participants and regulators, enabling better understanding of market dynamics and potentially improving forecasting accuracy and market stability. This research contributes to the growing field of explainable AI applications in energy markets by providing a transparent methodology that bridges the gap between black-box anomaly detection and actionable market intelligence.