Short-term Electricity Price Forecasting with Constrained Regressors

Conference · Thu Apr 18 04:00:00 EDT 2024

OSTI ID:2453902

Sun, Mucun ^[1]; Zhang, Li ^[2]

---

1. Idaho National Laboratory
2. The University of Texas at Rio Grande Valley

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 SEED-Forecsater can enhance forecasting performance, particularly in capturing electricity spikes, even under conditions without regressors during testing stage.

• Research Organization: Idaho National Laboratory (INL), Idaho Falls, ID (United States)
• Sponsoring Organization: 17
• DOE Contract Number: AC07-05ID14517
• OSTI ID: 2453902
• Report Number(s): INL/CON-24-77208-Rev000
• Country of Publication: United States
• Language: English

Related Subjects

24 POWER TRANSMISSION AND DISTRIBUTION
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION
42 ENGINEERING
deep learning
distribution shift
electricity price forecasting