摘要
提出了一种改进的BP神经网络学习算法,并将其应用于短期电力负荷预测中,通过采用基于响应函数输出限幅和自适应调整学习率等措施,来提高神经网络本身的效率和精度,仿真结果验证了改进措施的有效性,取得了满意的预测结果.
An improved BP neural network learning algorithm is applied to short-term load forecasting based on the response function output limiting and adaptive measures to improve the precision and efficiency of the neural network.The simulation result shows the validity of the improvement measures,which can achieve the satisfactory forecasting result.
出处
《上海电力学院学报》
CAS
2011年第1期14-18,共5页
Journal of Shanghai University of Electric Power
基金
上海市自然科学基金(09ZR1413200)
关键词
神经网络
BP算法
短期负荷预测
neural network
BP algorithm
short-term load forecasting
