中国电机工程学报2001，Vol.21Issue(4)：79-82,4.
基于组合式神经网络的短期电力负荷预测模型
SHORT-TERM LOAD FORECASTING WITH MODULAR NEURAL NETWORKS
陈耀武 1汪乐宇 1龙洪玉1
作者信息
- 1. 浙江大学仪器系，浙江 杭州 310027
- 折叠
摘要
Abstract
A novel short-term load forecasting model based on modular neuralnetworks is presented in this paper. The model employs fuzzy clustering analysis, pattern recognizing and neural networks to forecast hourly loads for the next hour to 24 hours out. The practical historical data within one year is divided into several groups by fuzzy clustering analysis. Each group is modeled by a separate module based on neural networks. During the forecasting phase, pattern recognizing is employed to activate the corresponding module for hourly loads forecasting. Using data from the Shaoxing utilities ,the satisfactory accurate results are obtained on the weekday , weekend and holidays . Moreover ,the model is robust ,and produces accurate results in some special cases.关键词
神经网络/模糊聚类分析/模式识别/短期电力负荷预测/电力系统分类
信息技术与安全科学引用本文复制引用
陈耀武,汪乐宇,龙洪玉..基于组合式神经网络的短期电力负荷预测模型[J].中国电机工程学报,2001,21(4):79-82,4.
