摘要
设计了一种基于反向传播(back propagation,BP)算法与模糊控制规则相结合的电力系统短期负荷综合预测模型,前者完成电力负荷中具有相对平稳的基本负荷分量,后者完成因天气变化、节假日等因素引起的随机负荷分量。由于模型引进了平滑系数、遗忘系数、不平均隶属函数等,提高了BP神经网络的学习速度,可充分反映负荷对温度的敏感性。
A short-term load forecasting model based on integration of back propagation（BP） algorithm with fuzzy rules is designed.The former fulfills base load components of relative stability in power load,and the latter fulfills random load components due to weather changes,holidays and so on.The introduction of smooth coefficient model,forgetting factor,unaverage subjection function improves learning speed of BP neural networks and fully reflects temperature sensitivity at load time.
出处
《广东电力》
2011年第4期49-53,共5页
Guangdong Electric Power
基金
广东技术师范学院科研项目(08kjy12)
