摘要
由于地区电网负荷受气象因素影响较大,提出了综合考虑气象因子的处理方法。该方法采用综合气象因子(人体舒适度和温湿指数)作为输入,克服了气象因子直接输入时输入量多、预测时间长的缺点。同时,针对BP神经网络动态性能的不足,建立基于Elman动态神经网络的短期负荷预测模型,并对该模型的激励函数和网络结构进行改进。改进后的模型考虑了电网的动态特性,减少了神经网络输入量,增强了负荷预测模型的适应性。采用杭州地区实际数据对提出的方法和模型进行验证,结果显示该方法和模型能明显提高负荷预测精度,表明该方法和模型是实用有效的。
Since the regional power load is significantly affected by weather factors, a method considering weather factors is proposed. This method uses comprehensive weather factors, namely the human body amenity indicator and THI （temperature and humidity index）, as inputs, which overcomes the disadvantages such as too many inputs and long forecasting time when weather factors are direct inputs. Besides, in view of the relatively low dynamic performance of BP neural network, a short-term load forecasting model based on Elman neural network is provided. Furthermore, improvements on excitation function and the structures of network have been made. The improved model considers the grid＇s dynamical performance, decreases the number of inputs and enhances the adaptability of the load forecasting model. This paper has verified the method and model using the data of Hangzhou. The results show that the method and model can significantly increase the precision of prediction, thus the method and model are practical and effective.
出处
《电力系统保护与控制》
EI
CSCD
北大核心
2012年第22期113-117,共5页
Power System Protection and Control
关键词
短期负荷预测
ELMAN神经网络
综合气象因子
激励函数
双隐含层
short-term load forecasting
Elman neural network
comprehensive weather factor
excitation function
two-hidden layer
