摘要
风能是一种清洁、无污染的可再生能源,由于气象参数的混沌和内在复杂性,使得风速的预测是一个非常困难的问题.基于对实际风速数据集,使用季节性指数调整消除季节性因子和反向传播(BP)神经网络,给出一种新的风速预测方法.数值结果表明,该方法能有效地提高风速预测的准确性.
Wind power is a clean and non-polluting renewable energy source. However,due to the chaotic and intrinsic complexity of weather parameters,the prediction of wind speed is a very difficult problem. In this paper,we propose a new hybrid wind speed forecasting method based on a back-propagation（ BP） neural network and the idea of eliminating seasonal effects from actual wind speed datasets using seasonal exponential adjustment. The numerical results indicate that the proposed method is effective in improving the accuracy of wind speed predictions.
出处
《四川师范大学学报（自然科学版）》
CAS
北大核心
2017年第2期272-276,共5页
Journal of Sichuan Normal University（Natural Science）
基金
国家自然科学基金(71471148)
甘肃省高等学校科研项目(2015A-150)
博士科研启动基金项目(xyby05)
关键词
BP神经网络
J-T检验
风速预测
绝对平均误差
BP neural network
J-T test
wind speed prediction
mean absolute percentage error
