Research On Power System Load Forecasting Based On Flower Pollination Algorithm And Neural Network

Electric power industry is an important basic industry related to national social and economic development and national people’s livelihood.Load forecasting is an important basic work of daily operation and control of power system.Accurate load forecasting is not only a necessary condition for power supply transaction plan and commissioning plan,but also can meet people’s daily demand for electricity,reduce the cost of power generation,and ensure the safe and stable operation of power grid.However,power load will be affected by many factors,such as temperature,season,political and economic policies and other factors,which will affect the prediction results,increasing the difficulty of power system load prediction.This paper describes the background and significance of load forecasting research in power system,and describes the research status at home and abroad around load forecasting and related forecasting methods.It studies the classification of load forecasting,the steps of establishing load forecasting model and the evaluation index of forecasting.Several widely used load forecasting technologies are listed.In order to improve the accuracy of the prediction model,the method of preprocessing the actual load data is studied.A load forecasting model based on flower pollination algorithm and feedforward neural network is established,and a single feedforward neural network method and the method in this paper are used to predict the actual power load of industrial park.Through comparative analysis,FPA-FNN method can effectively improve the overall forecasting performance of the algorithm.In order to further improve the accuracy of the prediction model,thispaper improves the traditional flower pollination algorithm based on clonal selection algorithm,and establishes a load prediction model based on improved flower pollination algorithm and generalized regression neural network.By comparing the prediction results with MATLAB software,it is found that the control parameters of MFPA-GRNN method are less,and the integration of clonal selection algorithm can effectively improve the convergence accuracy of pollination algorithm,and ensure the operation efficiency and prediction effect of the model.