Load Forecasting of Integrated Energy System Based on Dynamic FOA Optimized RBF Neural Network
Multivariate load forecasting of integrated energy system is one of the main ways to effectively improve energy utiliza-tion efficiency and reduce energy costs.In this paper,in response to the problem of complex data and difficulty in predicting and planning in integrated energy system,the dynamic FOA algorithm is introduced to optimize RBF neural network and assist in its optimization.Secondly meteorological factors is selected by using the Lasso principle,and the load data as well as meteoro-logical factors are inputted into the dynamic FOA optimized RBF neural network.Finally,a comprehensive energy system load prediction is conducted for a certain park in the north,and compare as well as verify with BP neural network.The prediction re-sults indicate that using this method for load forecasting can effectively improve the prediction effect and ensure the optimal op-eration of integrated energy system.
integrated energy systemload forecastingRBF neural networkFOA algorithm
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