Industrial Power Load Forecasting Method Based on Reinforcement Learning and PSO-LSSVM.

Area of Science:

• Electrical Engineering
• Artificial Intelligence
• Data Science

Background:

• Industrial power load forecasting is complex due to numerous influencing factors.
• Existing methods struggle with variations in load characteristics across different contexts.
• Accurate forecasting is crucial for efficient industrial energy management.

Purpose of the Study:

• To develop a novel, high-performance industrial power load forecasting method.
• To address the challenge of varying load characteristics in different regions, industries, and production patterns.
• To improve the accuracy and practical applicability of short-term load forecasting.

Main Methods:

• Improved K-means clustering for classifying historical load data into production patterns.
• A hybrid prediction algorithm combining reinforcement learning, particle swarm optimization, and least-squares support vector machine.
• Data-driven approach utilizing real-world datasets for training and validation.

Main Results:

• The proposed method effectively distinguishes load changes across different production patterns.
• High prediction accuracy was achieved by identifying specific load characteristics of diverse regions and industries.
• The algorithm demonstrated robust performance on real datasets, confirming its practical value.

Conclusions:

• The novel forecasting method offers a significant improvement over existing approaches.
• The integration of clustering and hybrid machine learning enhances forecasting precision.
• The approach provides a valuable tool for industrial energy management and planning.