A deep learning-based model for interval prediction of real-time clearing price in electricity market

With the rapid integration of renewable energy and the ongoing advancement of electricity market, accurate real-time prediction of electricity clearing prices has become increasingly critical. This paper introduces a deep learning-based framework for predicting real-time clearing price intervals, which employs time-shift feature construction and a greedy-based feature selection process to optimize high-dimensional feature sets. To address the nonlinear and non-stationary nature of price data, signal decomposition techniques are integrated to improve the extraction of underlying feature patterns. A point prediction model is first developed using a long short-term memory (LSTM) network, after which the upper and lower bounds of the prediction interval are derived via a similarity-based matching algorithm and statistical interval analysis, thereby explicitly quantifying market uncertainty. Numerical experiments based on the Shanxi Province (China) electricity market dataset show that the proposed model achieves higher interval coverage compared to baseline intervals, while maintaining practically useful interval widths, which offering more reliable decision support for electricity market participants.