A BP Neural Network Forecast Based On Different Optimization Algorithms And Its Application Taking Wind Speed As An Example

Along with the deepening of sustainable development in the energy field,the disadvantages of traditional energy in the resource content,the cost of the utilization as well as environmental pollution gradually appear,and the development and application of renewable energy has become the focus of world attention.Wind power stands out from various new energies with its easy access,high reserves and renewability.Wind speed is one of the crucial factors needed to be considered in wind energy related engineering research and the general operation of wind farms.Especially for wind power industry,wind speed will determine wind power that will affect grid operation.However,in actual operation,the volatility and intermittence of the wind speed can tend to increase the controllability difficulty of wind power,which will lead to the unstable power of the wind turbine,affect the quality of the output power and pose a threat to the security of power grid operation.Therefore,the short-term wind speed forecasting is an indispensable part in the wind power system.More accurate wind speed forecasting results can provide early warning for the power grid to facilitate the grid dispatching,so as to avoid the risk,reduce the loss and improve the efficiency of resource utilization.Thus,it is of great practical significance to develop high-accuracy wind speed forecasting method.For the short-term wind speed forecasting,researchers at home and abroad generally realized statistical forecasting according to historical data.In recent years,with the maturity of forecasting technology,the existing single statistical model has been unable to meet the forecasting accuracy requirements of relevant areas.In addition to improve the existing model,combining two or more methods to establish a new hybrid model has become a popular processing methods,including the use of intelligent optimization algorithm to optimize the parameters of neural network model.However,as the different selection of optimization algorithm and neural network forecasting model and different types of mixing models,the forecasting result will vary widely.How to choose the optimal model is not only a great discussable problem,but also a question of great value for actual wind speed forecasting.In order to solve the above problems,the short-term wind speed sequence is determined as the research object and the wind speed data(interval 10 minutes)from wind farm recorded by the 44thwind turbine in Penglai,Shandong Province in 2011 is selected in this paper.A large number of comparative experiments are carried out to search the more adaptive forecasting model to improve the forecasting accuracy and then improve the utilization rate of wind energy and provide numerical reference for the relevant areas.Therefore,accounting for the characteristics of wind speed and the applicability of neural network in nonlinear data forecasting,BP neural network is selected as the basis of forecasting model and the different optimization algorithms are combined with BPNN model in this paper.Accordingly,three kinds of hybrid forecasting models are put forward and applied to short-term wind speed forecasting in empirical Research.In this paper,the main work can be summarized as the following:First of all,in order to build the BP network,this paper made use of the longitudinal data set selection method,then classified the network as training set and testing set.Besides,the historical wind data used in experiment was divided into four seasons;the data of each season was pretreated by empirical mode decomposition(EMD)method respectively,which can transform original data into hierarchical IMF.By eliminating high-frequency noise,EMD can reduce the adverse effect of non-stationary signal on forecasting process.Then,in view of the deficiency in BPNN,which can easily fall into the local minimum point in time series forecasting,this paper combines BPNN model with different optimization algorithm,including cuckoo search(CS)algorithm,Firefly Algorithm(FA)as well as Harmony Search(HS)algorithm.And then,in consideration of the good results which FA brings to,this paper introduce the Intertia-Weight to improve global optimization ability of simple FA,building EMD-IWFA-BP model which can be applied to the actual short-term wind speed forecasting.At last,there were plenty of further discussions about above-mentioned experimental models.For example,it makes comparison with BPNN and Time Series Model(AR and ARMA),and discusses the forecasting effect of BPNN model under different parameter and the influence of different population size on the optimization algorithm convergence performance.Experimental results show that(1)the BPNN model forecasting accuracy is higher than Time Series Model(AR and ARMA),and three kinds of intelligent algorithms can further improve the forecasting accuracy of BPNN model;(2)Compared to three kinds of optimization algorithm,the optimization effect of firefly algorithm(FA)can get better performance on improving the forecasting accuracy and stability than the cuckoo algorithm(CS)and harmony search(HS)algorithm;(3)In the same conditions,the EMD-FA-BPNN model can get lower forecasting error than EMD-FA-BPNN model;(4)With the comparison of forecasting between one-step and mufti-step,the proposed optimizing models are more suitable for one-step wind speed forecasting.Based on the research results of a large number of domestic and foreign literatures,this paper systematically summarizes and thinks deeply about the relevant parts of this article,aiming at some innovation from the following perspectives:First,the optimization results of swarm intelligence algorithm and heuristic intelligent algorithm were compared in the form of percentage forecasting error by BP forecasting model,regarding Mean Absolute Percentage Error as the main evaluation standard,which can reflect the prediction accuracy so as to reflect the optimization ability of different algorithms.Second,Harmony Search algorithm,as one of the heuristic intelligent algorithms,has good application in many fields,but rarely involved in study of short-term wind speed forecasting.This paper applied HS into forecasting process of wind speed,optimizing the weights of BP network,in order to verify its effectiveness of improving the prediction precision.Third,under the condition of constant input layer,hidden layer,this paper compared and analyzed the forecasting results of the single-step and multi-step,which can illustrate the influence on prediction precision caused by internal behaviors of neural networks.Fourth,the paper made the further improvement of FA which had the best performance in former experiments.By introducing Intertia-Weight,the new case was solved by improved model of EMD-IWFA-BP.Although the experimental conclusion in this paper cannot represent the best short-term wind speed forecasting model,but can provide numerical reference for relevant departments or research area,so as to reduce the unnecessary loss of wind farm and power grid.