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Related Experiment Video

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Improved Neural Networks with Random Weights for Short-Term Load Forecasting.

Kun Lang¹, Mingyuan Zhang¹, Yongbo Yuan¹

¹Faculty of Infrastructure Engineering, Dalian University of Technology, Dalian, 116024, China.

|December 3, 2015

Summary

This study introduces an improved neural network with random weights (INNRW) for effective short-term load forecasting. The novel model optimizes input weighting for enhanced electric power system management efficiency.

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Area of Science:

Electrical Engineering
Artificial Intelligence
Data Science

Background:

Effective short-term load forecasting is crucial for electric power system management efficiency.
Existing forecasting models have limitations in accuracy and speed.

Purpose of the Study:

To propose a novel forecasting model, Improved Neural Networks with Random Weights (INNRW), for daily maximum load prediction.
To enhance the accuracy and efficiency of short-term load forecasting.

Main Methods:

Selected eight key factors as inputs for the forecasting model.
Employed a mutual information weighting algorithm to assign weights to input factors.
Utilized a novel neural network with random weights and kernels (KNNRW) for nonlinear function approximation.

Main Results:

The proposed INNRW model demonstrated superior performance compared to previously developed models in Dalian's daily load forecasting.
Simulation experiments confirmed the model's effectiveness in short-term load forecasting.

Conclusions:

The INNRW model offers a significant advancement in short-term load forecasting accuracy and efficiency.
This improved forecasting capability can enhance the overall management of electric power systems.