电力系统及其自动化学报2017，Vol.29Issue(7)：7-12,6.DOI:10.3969/j.issn.1003-8930.2017.07.002
应用共识PSO协同Trust-Tech方法的短期负荷预测
Consensus-based PSO-assisted Trust-Tech Method for Short-term Load Forecasting
摘要
Abstract
In order to improve the accuracy of short-term load forecasting,a novel neural network predictor,i.e.,en?hanced elite(E-Elite),is proposed in this paper based on a global optimization method with consensus-based particle swarm optimization-assisted trust-tech(CPSOATT). The E-Elite adopts a double-stage architecture:on the bottom stage,CPSOATT is employed to design a set of accurate and diverse sub-predictors with different optimal structures;on the top stage,sub-predictors are selected as hidden neurons,and an ensemble of sub-predictors is designed based on the structure of neural network,which can ensure the high computation performance of E-Elite on the whole by fully taking advantage of the diversity and accuracy of sub-predictors. Finally,the accurate short-term load forecasting re?sults are realized by using E-Elite based on data from an actual power system,and the comparison result indicates the correctness and validity of the proposed predictor.关键词
共识粒子群算法/人工神经网络/最优结构/短期负荷预测Key words
consensus-based particle swarm optimization(CPSO)/artificial neural network(ANN)/optimal structure/short-term load forecasting分类
信息技术与安全科学引用本文复制引用
张永峰,崔凯..应用共识PSO协同Trust-Tech方法的短期负荷预测[J].电力系统及其自动化学报,2017,29(7):7-12,6.基金项目
国家自然科学基金重点资助项目(51337007) （51337007）
