A Comprehensive Analysis of Imbalance Signal Prediction in the Japanese Electricity Market Using Machine Learning Techniques
Power system imbalances pose significant challenges to maintaining grid stability and ensuring efficient market performance, particularly in the context of the Japanese electricity market. The primary drivers of these imbalances are identified as the nonlinear responses of power generation and consu...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-05-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/18/11/2680 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Summary: | Power system imbalances pose significant challenges to maintaining grid stability and ensuring efficient market performance, particularly in the context of the Japanese electricity market. The primary drivers of these imbalances are identified as the nonlinear responses of power generation and consumer electricity demand to uncertain variables such as temperature and solar radiation, in addition to complex factors such as planned generator outages and operational constraints. Consequently, the prediction of imbalance signals using linear models is inherently challenging and requires the adaptation of more advanced methods in practice. This study comprehensively analyzes imbalance signal dynamics and develops practical forecasting tools using Machine Learning (ML) techniques. By incorporating a diverse range of features—including lagged imbalance data, weather forecast errors specific to Japan, and temporal patterns—we demonstrate that the prediction accuracy of imbalance signals is significantly improved compared to a baseline reflecting random forecasts based on class distribution observed during the initial training period. Furthermore, the proposed approach identifies the key drivers of hourly imbalance signals, while leveraging out-of-sample forecasting models. Based on these findings, we conclude that the use of multiple predictive models enhances the robustness and reliability of our forecasts, offering actionable tools for improving forecasting accuracy in real-world operations and contributing to a more stable and efficient electricity market. |
|---|---|
| ISSN: | 1996-1073 |