摘要
介绍了负荷预测和人工神经网络的相关知识。综合考虑了各个气象因素和综合气象指数对短期负荷预测的影响,设计了基于BP神经网络的短期负荷预测模型。利用人体舒适度和温湿指数进行预测,发现了理论预测和实际仿真的结果不同。研究发现由于风速的离散性和无规律性是导致误差的原因。以温湿指数作为输入,采用学习率可变的算法对杭州的负荷进行预测并进行了统计。
This paper first introduces the knowledge of the load forecasting and artificial neural network. Then the im- pacts of various weather factors on short-term load forecasting are comprehensively taken into account. A short-term load forecasting model was designed based on BP neural network. By using the human body amenity indicator and the temperature and humidity index in load forecasting, it was found that the theoretical predictions deviated from the sim- ulation results. Then the research revealed that the error was caused by the discrete nature and irregularity of the wind speed. Finally, the temperature and humidity index are used as an input variable to forecast the load of Hangzhou.
出处
《华东电力》
北大核心
2013年第5期1051-1055,共5页
East China Electric Power
关键词
短期负荷预测
人工神经网络
综合气象指数
风速
综合模型
short-term load forecasting
ANN
comprehensive weather index
wind speed
integrated model
