Reliable and accurate medium and long-term power load forecasting can enable power companies and generation companies to better allocate distribution networks and help them improve the protection and stability of the power system when renewable energy is connected to the grid.To this end,a medium and long-term power load forecasting scheme based on weighted least squares state estimation and fuzzy neural network was proposed.Based on the least squares state estimation,the power flow information obtained and the predicted load obtained from the neural network were used as inputs to the fuzzy neural network.Through the fuzzy neural network,high-quality medium and long-term power load prediction results were generated,and finally validated and evaluated on the IEEE 30 bus system.The experimental results show that the proposed scheme can achieve an average absolute percentage error of less than 2.55％,which is lower than the individual least squares state estimation method and fuzzy neural network method.
medium and long-term load forecasting/least squares state estimation/fuzzy neural network/data refinement/WLS-FNN load forecasting model/IEEE 30 bus system
维普
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