摘要
科学、准确的短期电力负荷预测有利于提高电力系统运行的经济性和安全性,向用户提供高质量的电力。提出一种基于改进型BP神经网络的短期负荷预测方法,并充分考虑建模时复杂气候敏感因数的影响,对输入样本的选取、预测模型的建立进行了论述。算例表明所提出方法具有较高的预测精度,负荷预测结果的相对误差小于3.63%。
Scientific and actual short-term load forecasting is benefit to improve electricity power system economically and safely,and provides high quality power energy. The paper brings forth a new type of method of short-term load forecasting based on BP neural network, takes complicative climate sensitive factor into account when establishing model. It discusses the selection of input sample and the construction of forecasting model. The example shows that the method is of high precision and the relative error is less than 3.63%.
出处
《电力需求侧管理》
北大核心
2005年第3期24-27,共4页
Power Demand Side Management
关键词
人工神经网络
误差反向传播算法
分段响应函数
惯性校正
artificial neural network
error backward propagation algorithm
segment response function
interia correction