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Power Load Probabilistic Prediction Based on Multi-Value Quantile Regression and Timing Fusion Ensemble Learning

Yuhang Liu¹, Fei Mei¹, Jun Zhang¹

¹School of Electrical and Power Engineering, Hohai University, Nanjing 211100, China.

Entropy (Basel, Switzerland)

|March 28, 2026

Summary

This summary is machine-generated.

This study introduces a new method for 10 kV bus load probabilistic prediction using multi-value quantile regression (MQR) and an ensemble learning model (ELM). The approach enhances prediction accuracy and reliability for distribution network scheduling.

Keywords:

ensemble learning load forecasting multi-value quantile regression probabilistic forecasting temporal fusion model

Related Experiment Videos

Area of Science:

Electrical Engineering
Data Science
Predictive Analytics

Background:

Accurate 10 kV bus load probabilistic prediction is crucial for safe distribution network scheduling.
Existing methods struggle with dynamic feature extraction and quantile crossing, impacting reliability.

Purpose of the Study:

To propose an improved 10 kV bus load probabilistic prediction method.
To enhance dynamic feature extraction and prediction reliability by addressing quantile crossing.

Main Methods:

Developed a temporal fusion ensemble learning model (ELM) integrating multiple temporal fusion network (TFN) sub-models.
Incorporated multi-value quantile regression (MQR) for synchronous multi-quantile forecasting.
Implemented a Listwise Maximum Likelihood Estimation (ListMLE) ranking constraint to optimize quantile ordering.

Main Results:

The MQR-ELM algorithm achieved a Prediction Interval Coverage Probability of 94.624% (near 95%).
Demonstrated a low Crossing Degree Index of 0.0476, significantly reducing quantile crossing.
Reported a Continuous Ranked Probability Score of 84.931, indicating superior performance.

Conclusions:

The proposed MQR-ELM method significantly improves probabilistic load forecasting accuracy and reliability.
The integration of MQR and ELM with ListMLE constraints effectively addresses limitations of existing methods.
This approach offers more interpretable and dependable predictions for distribution network operations.