基于BP神经网络的电力系统短期负荷预测  

Short-term Load Forecasting of Power System Based on BP Neural Network

作　　者：熊永胜[1] 

机构地区：[1]四川理工学院自动化与电子信息学院,四川自贡643000

出　　处：《成都大学学报（自然科学版）》2012年第2期167-169,共3页Journal of Chengdu University（Natural Science Edition）

摘　　要：电力系统短期负荷预测的准确性对电力系统的实时运行调度至关重要.采用BP神经网络对电力系统负荷短期预测研究,根据影响电力系统的负荷因素如温度、天气等确定模型构成,同时利用遗传算法对BP神经网络进行优化.实例表明,利用遗传算法优化的BP神经网络在电力系统短期负荷预测中是有效的.The accuracy of short-tenn electric load forecasting is very important for real-time operation in the power system. BP neural network was used to study short-term electric load in this paper. Structure of the model was detemained according to the power system load factors of temperature and weather. Genetic algorithm was used to optimize the BP neural network. The examples show that genetic algorithm optimized by BP neural network is effective in the short-term load forecasting of power system.

关 键 词：负荷预测 神经网络 遗传算法 

分 类 号：TM34[电气工程—电机]