摘要
根据短期电力负荷预测的特点,将遗传算法和人工神经网络相结合,提出一种负荷预测新算法─—遗传神经网络优化预测方法。该方法明显地提高了模型的优化能力,有效地克服了人工神经网络学习速度慢、存在局部极小点的固有缺陷。经实例验证,该方法能有效地提高预测精度和速度。
According to the characteristics of the short-time power load forecasting, the authors propose a newload forecasting model based on the combination of genetic algorithm and neural network. The intrinsic defectsof artificial neural network, such as its slow learning speed and local minimum points, are overcome. It can beseen horn a case study that the proposed method can improve the accuracy and the speed of load forecasting effectively.
出处
《华北电力大学学报(自然科学版)》
CAS
北大核心
2001年第1期1-5,共5页
Journal of North China Electric Power University:Natural Science Edition
基金
国家自然科学基金资助项目!(50077007)
国家电力公司重点学科建设资金资助项目!(A99B03).