魏立兵,赵峰,王思华.基于人群搜索算法优化参数的支持向量机短期电力负荷预测[J].电测与仪表,2016,53(8):. Wei Li-bing,Zhao Feng,Wang Sihua.Based on population search algorithm to optimize the parameters of support vector machine for Power short-term load forecasting[J].Electrical Measurement & Instrumentation,2016,53(8):.
基于人群搜索算法优化参数的支持向量机短期电力负荷预测
Based on population search algorithm to optimize the parameters of support vector machine for Power short-term load forecasting
Support Vector Machine(SVM) is a new kind of machine learning algorithm, which is based on structural risk minimization criterion to obtain smaller actual risk, effectively improve the generalization ability, has the theory tightly, strong adaptability, the characteristics of global optimization, widely used in pattern recognition and regression problems. The paper is based on the historical load data as input, through the Seeker Optimization Algorithm(SOA) to search algorithm on the parameters of support vector optimization, and get the optimal parameter selection, and then put the optimal parameter generation into the SVM prediction model, get the Seeker Optimization Algorithm of support vector machine (SOA-SVM) model, by using this model for the next 24 hours in one region of the load for short-term prediction. Through the example, the use of SOA-SVM prediction precision accuracy is higher than BP neural network and PSO-SVM,so that use this method to short-term load forecasting is feasible and effective.