RESEARCH ON PV SYSTEM POWER PREDICTION BASED ON IMPROVED SIMILAR DAY AND HBA-BiLSTM-KELM NEURAL NETWORK
In order to improve the output power prediction accuracy of PV generation system,this paper proposed a prediction model based on improved similar days and honey badger algorithm to improve bidirectional long-short term memory neural network and kernel extreme learning machine.Firstly,The CRITIC weight method is used to dynamically calculate the influence weight of each meteorological factor on photovoltaic power generation.The similar days are determined by calculating the weighted Euclidean distance between the historical days and the predicted days.Secondly,HBA is used to optimize the parameters of BiLSTM and KELM models,and then the BiLSTM optimized by HBA parameters is used for power prediction,and the optimized KELM is used for error optimization prediction.Finally,the preliminary prediction power and the error prediction power are added to obtain the final prediction power.The simulation results show that the average absolute percentage error of the model is 0.91%,which has high prediction accuracy of output power of photovoltaic system.
PV power generationpower forecastingneural networkkernel extreme learning machinehoney badger algorithm
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