基于自组织特征神经网络和最小二乘支持向量机的短期电力负荷预测方法

准确的负荷预测对于整个电力系统经济有效运行有着重要的意义.针对负荷预测集和预测模型作出协同优化改进,提出了基于粒子群(Particle Swarm Optimization,PSO)优化的自组织特征映射网络(Self-organizing Feature Mapping,SOFM)和遗传算法(Genetic Algor...

Short-Term Load Forecasting Method Based on Self-Organizing Feature Mapping Neural Network and GA-Least Square SVC Model

Accurate load forecasting is very important for economic and effective operation of whole power grid.To make collaborative optimization improvement for load orecasting set and the forecasting model,based on particle swarm optimization-self-organizing feature mapping(PSO-SOFM)and genetic algorithm-based least square support vector machine(GALSSVM)a short-term power load forecasting method was proposed.The adaptive weighted PSO was used to optimize the weight of SOFM neural network,and the optimized PSOSOFM neural network was used to classify the sort processing the original load date to obtain multi groups of training sets.For each group of training set a least square support vector machine forecasting model was established and its key parameters were optimized by GA,finally a GA-LSSVM forecasting model was obtained.Finally,performing load forecasting by existed load data,the effectiveness and accuracy of the proposed method are verified.