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Multiobjective Optimal Dispatching of Smart Grid Based on PSO and SVM
Author(s) -
Bao Man,
Hongqi Zhang,
Hao Wu,
Chao Zhang,
Zixu Wang,
Xiaohui Zhang
Publication year - 2022
Publication title -
mobile information systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.346
H-Index - 34
eISSN - 1875-905X
pISSN - 1574-017X
DOI - 10.1155/2022/2051773
Subject(s) - computer science , microgrid , particle swarm optimization , support vector machine , mathematical optimization , smart grid , grid , realization (probability) , energy (signal processing) , artificial intelligence , machine learning , control (management) , ecology , statistics , geometry , mathematics , biology
The optimization of microgrid is an important part of smart grid. The global energy consumption is seriously greater than the energy it has, and the environmental pollution brought by it should not be underestimated. If we want to reduce their impact, introducing the optimization of microgrid is a good solution. Short-term load forecasting is a very important prerequisite for microgrid optimization, which lays a solid foundation for the realization of the development goal of environmental protection and the improvement of the economic benefits of microgrid. In this paper, a Multi-PSO-SVM forecasting model is proposed to forecast the actual load. By simulating four prediction models with three different samples, we can see that the average predicted value and actual load value of Multi-PSO-SVM algorithm in the three different samples are almost less than 10 MV. Compared with the other three algorithms, Multi-PSO-SVM is superior in accurately predicting the load value at each time point, which provides important conditions for the success of microgrid optimization.

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