Optimal Parameter Selection for Support Vector Machine Based on Artificial Bee Colony Algorithm: A Case Study of Grid-Connected PV System Power Prediction.

Xiang-Ming Gao,San-Bo Pan,Shi-Feng Yang

doi:10.1155/2017/7273017

Abstract

Predicting the output power of photovoltaic system with nonstationarity and randomness, an output power prediction model for grid-connected PV systems is proposed based on empirical mode decomposition (EMD) and support vector machine (SVM) optimized with an artificial bee colony (ABC) algorithm. First, according to the weather forecast data sets on the prediction date, the time series data of output power on a similar day with 15-minute intervals are built. Second, the time series data of the output power are decomposed into a series of components, including some intrinsic mode components IMFn and a trend component Res, at different scales using EMD. The corresponding SVM prediction model is established for each IMF component and trend component, and the SVM model parameters are optimized with the artificial bee colony algorithm. Finally, the prediction results of each model are reconstructed, and the predicted values of the output power of the grid-connected PV system can be obtained. The prediction model is tested with actual data, and the results show that the power prediction model based on the EMD and ABC-SVM has a faster calculation speed and higher prediction accuracy than do the single SVM prediction model and the EMD-SVM prediction model without optimization.

Highlights

With the increasing scale of grid-connected PV systems, the adverse effects of intermittent and uncertain characteristics of the PV system on the public grid are becoming increasingly important [1,2,3]
An output power prediction model of a gridconnected PV system is proposed in this paper to optimize the support vector machine (SVM) with the artificial bee colony algorithm
To verify the performance of the output power prediction model for a PV system based on empirical mode decomposition (EMD) and artificial bee colony (ABC)-SVM, the Matlab software is used to complete the model construction

Summary

Introduction

With the increasing scale of grid-connected PV systems, the adverse effects of intermittent and uncertain characteristics of the PV system on the public grid are becoming increasingly important [1,2,3]. Direct prediction methods do not require solar irradiance data and can predict the power output of PV power generation systems in the time period by using only the historical PV system data and public weather information [5,6,7,8]. The advantages of different algorithms are combined to establish and test the prediction model for the output power of grid-connected PV systems based on the EMD and ABC-SVM. This model effectively overcomes the defects, such as poor generalization performance, low prediction accuracy, and unstable prediction results, that are observed when a single model is adopted and successfully applies the artificial bee colony optimization algorithm and EMD method to predict the output power of grid-connected PV systems. After the clustering is complete, the prediction day in the same class and its similar historical date can be obtained according to the partition matrix U

Decomposing the Output Power Signal of the Grid-Connected PV System by EMD

Artificial Bee Colony Algorithm and Support Vector Machine

Constructing a PV Power Prediction Model Based on EMD and ABC-SVM

Simulation Results of the Case Study

Conclusions