Short term load forecasting of RF-BILSTM optimized by Sparrow Search Algorithm
Accurate forecasting of power load is an important guarantee for the safe operation of power grid and normal production of society.However,the accuracy of forecasting accuracy is greatly reduced due to the nonlinearity of load data and the uncertainty of many influencing factors.Therefore,in order to improve the accuracy of short-term power load forecasting,a forecasting model based on Sparrow Search Algorithm(SSA)optimized Random Forest(RF)-Bidirectional Long Short-term Memory Neural Network(BILSTM)is proposed.Firstly,in response to issues such as the construction of power load data features,the data is quantified and standardized for preprocessing operations to facilitate subsequent model input.Secondly,the RF algorithm is used to rank the importance of numerous influencing factors of power load,retain important factors,and further combine them with historical load data to form the final input of the neural network.Finally,SSA algorithm is used to optimize the selection of some hyperparameter of BILSTM model,which solves the problem of manual selection.By comparing with other models,it is verified that this model has high prediction accuracy.
random forestimportance rankingload forecastingsparrow algorithmlong short-term memory neural network
NETL
NSTL
万方数据
维普
