Electric Power Load Forecasting Method Based on a Support Vector Machine Optimized by the Improved Seagull Optimization Algorithm

Accurate load forecasting is conducive to the formulation of the power generation plan, lays the foundation for the formulation of quotation, and provides the basis for the power management system and distribution management system. This study aims to propose a high precision load forecasting method...

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Vydáno v:Energies (Basel) Ročník 15; číslo 23; s. 9197
Hlavní autoři: Zhang, Suqi, Zhang, Ningjing, Zhang, Ziqi, Chen, Ying
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 01.12.2022
Témata:
Accuracy
Algorithms
Artificial intelligence
China
Consumption
electric management system
Electric power
Electric power production
Electric power systems
Electricity
Electricity distribution
Energy consumption
Forecasting
Gulls
Improved Seagull Optimization Algorithm
Literature reviews
Mathematical optimization
Methods
Neural networks
Optimization algorithms
Planning
power load forecasting
Power supply
Regression analysis
Seagull Optimization Algorithm
Support Vector Machine
Support vector machines
Time series
China
ISSN:1996-1073, 1996-1073
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Shrnutí:Accurate load forecasting is conducive to the formulation of the power generation plan, lays the foundation for the formulation of quotation, and provides the basis for the power management system and distribution management system. This study aims to propose a high precision load forecasting method. The power load forecasting model, based on the Improved Seagull Optimization Algorithm, which optimizes SVM (ISOA-SVM), is constructed. First, aiming at the problem that the random selection of internal parameters of SVM will affect its performance, the Improved Seagull Optimization Algorithm (ISOA) is used to optimize its parameters. Second, to solve the slow convergence speed of the Seagull Optimization Algorithm (SOA), three strategies are proposed to improve the optimization performance and convergence accuracy of SOA, and an ISOA algorithm with better optimization performance and higher convergence accuracy is proposed. Finally, the load forecasting model based on ISOA-SVM is established by using the Mean Square Error (MSE) as the objective function. Through the example analysis, the prediction performance of the ISOA-SVM is better than the comparison models and has good prediction accuracy and effectiveness. The more accurate load forecasting can provide guidance for power generation and power consumption planning of the power system.
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ISSN:1996-1073
1996-1073
DOI:10.3390/en15239197