电力系统保护与控制2011，Vol.39Issue(21)：65-69,5.
灰色BP神经网络模型的优化及负荷预测
Optimization and power load forecasting of gray BP neural network model
摘要
Abstract
In order to enhance the forecasting accuracy of medium and long-term power load forecasting under the conditions of small sample data, the shortcomings of traditional GM (1,1) forecasting model are analyzed. A GM (1,1) optimization modeling method for medium and long-term load forecasting is proposed. By use of a continuous function whose form is the same as the GM (1,1) time response formula, the raw discrete data is fitted. The continuous function is mapped to a BP neural network, and the corresponding relation between GM (1,1) model gray parameters and BP network weights is established. With known load data as training sample, the network is optimized by means of BP algorithm, when the BP network convergence, optimized gray parameters can be extracted, therefore, the optimization modeling of GM (1,1) for medium and long-term load forecasting is realized. The example results show that the method is feasible and effective.关键词
GM (1,1)模型/灰色BP神经网络模型/BP算法/优化建模/中长期负荷预测Key words
GM (1, 1) model/gray BP neural network model/BP algorithm/optimized modeling/medium and long-term load forecasting分类
信息技术与安全科学引用本文复制引用
周德强,武本令..灰色BP神经网络模型的优化及负荷预测[J].电力系统保护与控制,2011,39(21):65-69,5.基金项目
湖北省教育厅科学技术研究项目(D20111305,Q20L01309) （D20111305,Q20L01309）
