To address the problem of large load forecasting errors caused by large medium and long-term load fluctuations and uncertain factors in power systems,a medium and long-term load forecasting method for power systems based on multivariable phase space reconstruction(MPSR)and improved restricted boltzmann machines(IRBM)is proposed.First,the multiple linear regression analysis method is used to analyze the correlation between weather factors and electrical load,and it is combined with the electrical load sequence to form a multivariate time series.Second,the C-C method is used to determine the optimal embedding dimension and time delay of each time series,to achieve multivariable phase space reconstruction.Finally,the data set established by multivariable phase space reconstruction is used to train the power system load forecasting model.At the same time,the gradient optimization method is used to optimize the parameters to obtain the forecasting model.The results show that compared with long short-term memory neural networks and particle swarm optimization BP neural networks,it is confirmed that the proposed forecasting model can significantly improve forecasting accuracy.
维普
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