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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Veröffentlicht in:	Electronics (Basel) Jg. 11; H. 22; S. 3834
Hauptverfasser:	Li, Chao, Guo, Quanjie, Shao, Lei, Li, Ji, Wu, Han
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.11.2022
Schlagworte:	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
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Abstract	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.
AbstractList	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.
Audience	Academic
Author	Wu, Han Li, Chao Li, Ji Guo, Quanjie Shao, Lei
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SubjectTerms	Algorithms Artificial intelligence Cyclic loads Deep learning Forecasting Mathematical models Mathematical optimization Neural networks Optimization Prediction theory Time series
Title	Research on Short-Term Load Forecasting Based on Optimized GRU Neural Network
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