Short-Term Electric Load Prediction and Early Warning in Industrial Parks Based on Neural Network

This paper proposes a load forecasting method based on LSTM model, fully explores the regularity of historical load data of industrial park enterprises, inputs the data features into LSTM units for feature extraction, and applies the attention-based model for load forecasting. The experiments show t...

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Bibliographic Details
Published in:Discrete dynamics in nature and society 2021, Vol.2021, p.1-10
Main Authors: Wang, Guannan, Yang, Pei, Chen, Jiayi
Format: Article
Language:English
Subjects:
Accuracy
Consumption
Decomposition
Deep learning
Electrical loads
Electricity distribution
Feature extraction
Forecasting
Holidays & special occasions
Industrial districts
Industrial parks
Machine learning
Mathematical models
Methods
Neural networks
Optimization algorithms
Prediction models
Regression analysis
Support vector machines
Time series
Wavelet transforms
Wind farms
Wind power
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Summary:This paper proposes a load forecasting method based on LSTM model, fully explores the regularity of historical load data of industrial park enterprises, inputs the data features into LSTM units for feature extraction, and applies the attention-based model for load forecasting. The experiments show that the accuracy of our prediction model and early warning model is better than that of the baseline and can reach the standard of application in practice; this model can also be used for early warning of local sudden large loads and identification of enterprise power demand. Therefore, the validity of the method proposed in this paper is verified using the historical dataset of industrial parks, and relevant technical products and business models are formed to provide value-added services to users by combining existing practical cases for the specific scenario of industrial parks.
ISSN:1026-0226
1607-887X
DOI:10.1155/2021/1435334