Research on Short-Term Load Forecasting Based on Optimized GRU Neural Network

Accurate short-term load forecasting can ensure the safe and stable operation of power grids, but the nonlinear load increases the complexity of forecasting. In order to solve the problem of modal aliasing in historical data, and fully explore the relationship between time series characteristics in...

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Bibliographic Details
Published in:Electronics (Basel) Vol. 11; no. 22; p. 3834
Main Authors: Li, Chao, Guo, Quanjie, Shao, Lei, Li, Ji, Wu, Han
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.11.2022
Subjects:
Algorithms
Artificial intelligence
Cyclic loads
Deep learning
Forecasting
Mathematical models
Mathematical optimization
Neural networks
Optimization
Prediction theory
Time series
China
ISSN:2079-9292, 2079-9292
Online Access:Get full text
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Summary:Accurate short-term load forecasting can ensure the safe and stable operation of power grids, but the nonlinear load increases the complexity of forecasting. In order to solve the problem of modal aliasing in historical data, and fully explore the relationship between time series characteristics in load data, this paper proposes a gated cyclic network model (SSA–GRU) based on sparrow algorithm optimization. Firstly, the complementary sets and empirical mode decomposition (EMD) are used to decompose the original data to obtain the characteristic components. The SSA–GRU combined model is used to predict the characteristic components, and finally obtain the prediction results, and complete the short-term load forecasting. Taking the real data of a company as an example, this paper compares the combined model CEEMD–SSA–GRU with EMD–SSA–GRU, SSA–GRU, and GRU models. Experimental results show that this model has better prediction effect than other models.
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ISSN:2079-9292
2079-9292
DOI:10.3390/electronics11223834