Interpretable Interval Prediction-Based Outlier-Adaptive Day-Ahead Electricity Price Forecasting Involving Cross-Market Features

Electricity prices behave more irregular patterns due to uncertainties and effects of mixed-temporal primary energy markets. Thus, it is challenging to precisely forecast them. To conquer this barrier, an interpretable interval prediction method that seamlessly unifies cross-energy and electricity markets is proposed. At the outset, to clarity the feature rising the aberrant electricity prices, several exogenous, and multitemporal features from other primary energy markets, such as natural gas and coal markets, are unified to settle our database. Then, a Gaussian mixture model (GMM)-lightweight gradient boosting machine hybrid detector is presented to isolate and foresee the outlier sequence of electricity prices. A hybrid LSTNet-kernel density estimation (LSTNet-KDE) method is further proposed to enable outlier-adaptive interpretable interval prediction. Specifically, the LSTNet contributes to amalgamating multitemporality across markets and predicting the principal trends, and the KDE serves to encapsulate the uncertainty for the GMM-foreseen outliers. The method further merges with the Shapley additive explanations technique, such that exogenous latent features that induce electricity prices outliers can be finally comprehended. The numerical study on the real-world Danish electricity market verifies that, our proposed method beats other rivals in terms of precision, especially notable in forecasting outliers of electricity prices.