Multi-wind Field Output Power Prediction Method based on Energy Internet and DBPSO-LSSVM

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1 , pp Multi-wind Field Output Power Prediction Method based on Energy Internet and DBPSO-LSSVM *Jianlou Lou 1, Hui Cao 1, Bin Song 2, Jizhe Xiao 1 1 School of Information Engineering, Northeast DianLi University, Jilin 1312, China 2 State Grid Jilin Electric Power Co.Ltd., Jilin power Supply Company Abstract: Traditional wind power prediction method only applicable to a single wind farm. In view of this isolated prediction model. In this paper, we propose a multi-wind farm prediction method based on DBPSO-LSSVM and energy Internet. Firstly, based on DBSCAN algorithm to detect outliers and select multi-wind field training samples. And to search the optimal input parameters of LSSVM based on particle swarm algorithm. Secondly, to predict multiple wind field s power combined with numerical weather prediction system. The method we proposed can be used to make the scheduling plan in advance to solve a large number of abandoned wind power rationing problem every year. In experiment, the method has lowest error rate compares to LSSVM and BP neural network and it is more suitable to predict wind fields in different areas. Keywords: Multiple wind farms power prediction; Energy Internet; Least square support vector machine; particle swarm; wind power utilization 1 Introduction Wind energy as a renewable energy has been paid more and more attention. However, wind power itself has inherent shortcomings such as intermittent and randomness. The prediction model is isolated and the capacity of wind power utilization is limited in some areas. So every year there are a serious of problems of abandoned wind power rationing. This waste a lot of wind power resource. At present, the following methods are proposed to predict the wind farm power: In paper [1], predict wind farm power based on component Bayesian method. In paper [2], predict wind farm power based on error stack correction. In paper [3], predict wind farm power based on Particle Swarm Optimization for nuclear extreme learning machine model. In paper [4], predict wind farm power based on statistical method. In paper [5], predict wind farm power based on Markoff chain principle and Correlation vector. In paper [6], propose to use (OS-ELS) learning machine to modify the wind speed and achieve the short-term wind prediction. In paper [7], propose to use Tuple vector time warping to predict wind farm power. However, the current method can only predict a single wind farm.with the rapid development of energy Internet, the efficient and fast data interconnection, information sharing platform has a great impact on the traditional isolated operation mode [8],[9]. In this paper, we propose a multiple wind farms prediction method. ISSN: ASTL Copyright 16 SERSC

2 Firstly, we introduce some relate works. Secondly, we introduce the prediction process. Finally, the effectiveness of method is proved by experiment. 2 Relate Work 2.1 Relate Technologies and Characteristics based on Energy Internet Firstly, the method we propose based on the characteristics such as renewable energy sources, information openness and sharing, advanced information processing technology, energy free transmission and advanced storage technology of Energy Internet to predict multiple wind fields. In order to make wind power scheduling plan in advance, to resolve the wind power utilization limited [1-11]. 2.2 DBSACN Outlier Detection Theory DBSCAN algorithm is a well-known clustering algorithm that according to two parameters Eps and MinPts to identify a cluster [12]. Due to the noise of real data is very large, it will affect the accuracy of the prediction model. So we remove outliers based on DBSCAN algorithm. The steps as follows: (1) Remove the point of less than or equal to zero to exclude human factors; (2) Set the parameters Eps and MinPts of DBSCAN algorithms and to cluster; (3) Among the clusters output, the number of data points in the Eps radius is larger than the MinPts labeled as the training sample data, the others are labeled as outliers; (4) Remove outliers and the remaining data are the training sample data. 2.3 PSO-LSSVM Regression Theory LS-SVM is an improvement of SVM and maps the training sample data to high dimensional feature space by a nonlinear mapping[13].and then, based on Particle Swarm Optimization(PSO)[14] to select parameters γ and σ2 of LSSVM, Specific steps are as follows: (1) Initial accelerate constant, inertia weight, population size,maximum number of evolution, parameters γ and σ2 and the parameters are mapped into random particles. (2) According to current position to calculate optimal position of current point and group.update the speed and position of each particle, and generate new population. (3) Calculate new location and adaptive value of each particle, compare its best position and optimal location in history. If better, replace it, otherwise, not change. (4) Check whether to meet the end of the optimization conditions, if satisfy, output the best nuclear parameters of LS-SVM, otherwise, turn to step (2). Copyright 16 SERSC 129

3 3 Multi-wind Farm Output Power Prediction Method based on Energy Internet and DBPSO-LSSVM 3.1 Selection and Normalization of Input Parameters The wind speed, air density and wind direction are parameters which have great influence on output power of wind turbine [15], [16]. So the data corresponding to the three attributes and power attribute are used as the initial input data in this paper. In order to eliminate the influence of the volume, the input data normalized to the [, 1] range, the normalization formula is as follows: x * x (1) In formula: x* is normalized initial training sample data; u is mean of all sample data; σ is Standard deviation of all sample data. 3.2 Establishment of Prediction Model based on Energy Internet and DBPSO-LSSVM The specific steps of DBPSO-LSSVM are as follows: (1) Collect multiple wind field SCADA data sets under energy Internet environment based on the features in 2.1 chart. And Normalization. (2) Multiple wind field initial sample data sets are input DBSCAN algorithm. Based on the 2.2 step to detect outliers, and select the training sample data sets. (3) To establish PSO-LSSVM models based on 2.3 step. (4) At last, multiple wind farm prediction models are established and combined with NWP system to predict. Wind field 1 data Wind field 2 data Wind field n data Wind field 1 data Wind field 2 data Wind field n data Initialize particle swarm Based on DBSCAN clustering Adaptive weight Energy Internet Based on DBPSO-LSSVM regression prediction Greater than the parameter Minpts Y Training sample N outliers Update speed and position Meet the end conditions Y Output optimized LSSVM parameters N NWP System Wind field 1 model Wind field 2 model Wind field n model Wind field 1 training samples Wind field 2 training samples Initialize LSSVM parameters Wind field n training samples Re training Scheduling personnel Predictive value 1 Predictive value 2 Predictive value n Wind field 1 prediction model Wind field 2 prediction model Wind field n prediction model Fig.1. Integrated prediction From figure 1, the method we propose to share the multiple wind farms SCADA data in different regions under the environment of energy Internet. And when the SCADA data of n wind fields are input, the n regression models will be output. This 13 Copyright 16 SERSC

4 is due to the method will be selected different training samples and different optimal input parameters among different wind fields. At last, it will be output different prediction models aim at different data distribution of wind farms. 4 Example analysis In experiment, we predict two wind farms in North and South China based on the method we propose with MATLAB simulation technology. The value of wind speed, wind direction and air density are as the input data based on numerical weather prediction system to provide. The output data are predict values each wind field. 4.1 Comparison of Prediction Results of Wind Power Based on the method we propose to predict two wind farms power compare to BP neural network, LSSVM and actual output power, the result as shown in figure 2: 9 Actual power BP-neural network DBPSO-LSSVM 9 Actual power LSSVM DBPSO-LSSVM Actual power BP-neural network DBPSO-LSSVM 1 Actual power LSSVM DBPSO-LSSVM Fig.2. Comparison of DBPSO-LSSVM,LSSVM,BP neural network, actual output power From figure 2, the method we propose has the highest prediction accuracy and the predict values close to actual power mostly between two wind farms in different areas. Remove outliers of SCADA data and select the optimal input parameters can improve the precision of forecasting when establish model based on LSSVM. 4.2 Comparison of Output Power Prediction Error In this paper, the mean square error (MSE) and the mean absolute percentage error (MAPE) are used to evaluate the prediction results. Copyright 16 SERSC 131

5 n 2 Pt Pt 1 E MSE (2) n t1 n 1 Pt Pt E MAPE (3) n P t1 t In formula: P is the real value of the forecast; P^ is predictive value of prediction; n is data quantity. Table 1. Main error indicators of the prediction models of A and B wind fields Algorithm type(a) MSE MAPE Algorithm type(b) MSE MAPE BP-neural network BP-neural network LSSVM LSSVM DBPSO-LSSVM DBPSO-LSSVM From Table 1: through the MSE and the MAPE two common error indicators calculated, the method we propose in this paper has the smallest error index. And the fluctuation range of error index between two wind fields is the smallest of all. So the proposed method is stable to different data distribution and more suitable than the other two methods to predict the output power of different wind fields. 5 Conclusion In this paper, based on the information sharing and interconnection of energy Internet, we propose a multiple wind fields prediction method based on energy Internet and DBPSO-LSSVM. The method we propose takes account into data distribution of wind farm in different areas. And to establish the best forecasting models for different wind fields. It has the smallest error compared to LSSVM, BP neural network. The fluctuation range of MSE and MAPE between two wind fields are.4 and.7 up and down. It is more stable. So the method provides a basic research for future to realize multiple wind farms prediction under the energy Internet environment. References 1. Ming, Y., Shu, F., Xueshan, H.: Probabilistic forecasting method for output power of wind farm based on component sparse Bayesian learning [J]. Automation of electric power system, 12, 36(14): Mao, M., Cao, Y., Zhou, S.: Improved short term wind power prediction method based on error stack correction [J]. Automation of electric power system, 13, 37(23): 34G Copyright 16 SERSC

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