基于混合粒子群算法的短期负荷预测模型  

Hybrid Optimization Method Based on Evolutionary Algorithm and Particle Swarm Optimization for Short-term Load Forecasting

作　　者：王波[1] 邰能灵[1] 翟海青[2] 叶剑[2] 朱家栋[3] 漆梁波[3] 

机构地区：[1]上海交通大学电气工程系,上海200240 [2]上海市电力公司调度通信中心,上海200035 [3]上海中心气象台,上海200030

出　　处：《电力系统及其自动化学报》2008年第3期50-55,共6页Proceedings of the CSU-EPSA

摘　　要：由于电力负荷内在的非线性特性,传统基于梯度搜索的参数辨识技术可能陷入局部最优,影响了预测精度,故提出了混合进化和粒子群优化算法。将进化算法的基本思想引入粒子群优化算法,不但保持了粒子群算法结构简单、易于实现的特点,而且充分发挥了进化算法的全局搜索能力,可有效提高算法的精度和收敛速度。对上海地区电网进行短期负荷预测,与实际值相比较,结果表明,该算法具有较高的预测精度,是一种有效的短期预测方法。Owing to the inherent nonlinear characteristics of the power system loads, solutions of the gradient search based technique may stall at the local optimal points, which lead to high forecasting error. A hybrid method based on evolutionary algorithm and particle swarm optimization （HPSO） to identify the auto- regressive and moving average with exogenous variables （ARMAX） is proposed. HPSO introduces evolutionary algorithm into particle swarm optimization （PSO）, thus can get higher precision and faster convergence speed without changing the structure of PSO. The proposed method has been tested on the Shanghai power system, and the results show that the proposed approach can achieve great accuracy.

关 键 词：外源自回归动平均 进化算法 粒子群优化 短期负荷预测 

分 类 号：TM714[电气工程—电力系统及自动化]