Short-term Electricity Price Forecasting with Constrained Regressors

Authors

• Mucun Sun, Idaho National Laboratory
• Li Zhang, The University of Texas Rio Grande Valley
• Yifeng Gao, The University of Texas Rio Grande Valley

Document Type

Conference Proceeding

Publication Date

4-2026

Abstract

The volatility of electricity price presents a challenge to market participants as their decision-making process are highly depend on the accuracy of price forecasts. However, there is growing empirical evidence of increasing price volatility and price spikes in electricity markets as a result of variable renewable energy generation, extreme weather events, and other factors. The distribution shift caused by spikes in electricity price data differentiates the forecasting tasks from other renewable energy sources. Moreover, the observations may be compromised by cyberattacks and thus not available in the testing phase. To this end, we propose a Similarity-Enhanced Electricity Decomposition Forecasting model (SEED-Forecaster) to address the missing response problem and spikes capturing in short-term electricity price forecasting. The effectiveness of the proposed framework is tested on real-world electricity price data from California Independent System Operator (CAISO). Numerical results of case studies show that the proposed SEEDForecsater can enhance forecasting performance, particularly in capturing electricity spikes, even under conditions without regressors during testing stage.

Comments

©2026 IEEE

Recommended Citation

M. Sun, L. Zhang and Y. Gao, "Short-term Electricity Price Forecasting with Constrained Regressors," 2026 IEEE Green Technologies Conference (GreenTech), Boulder, CO, USA, 2026, pp. 1-5, https://doi.org/10.1109/GreenTech68285.2026.11471580

Publication Title

2026 IEEE Green Technologies Conference (GreenTech)

DOI

10.1109/GreenTech68285.2026.11471580