广东电力2017，Vol.30Issue(4)：55-60,6.DOI:10.3969/j.issn.1007-290X.2017.04.010
基于改进灰色神经网络组合模型的光伏电站短期出力预测
Short-term Output Forecasting for Photovoltaic Power Station Based on Improved Grey Neural Network Combined Model
刘博洋 1潘宇 1许伯阳 1刘文 1李焕奇 2王苏3
作者信息
- 1. 东北电力大学 电气工程学院,吉林 吉林 132012
- 2. 国网吉林省电力有限公司吉林供电公司,吉林 吉林 130021
- 3. 国网浙江德清县供电公司,浙江 湖州 313200
- 折叠
摘要
Abstract
In allusion to the problem that defects of the grey model and the neural network model have influences on forecasting precision of the traditional grey neural network combined model, this paper presents a kind of short-term forecasting method of photovoltaic power based on improved grey neural network model.By taking the highest and the lowest temperature and power data in history days as input, it combines the improved grey model and neural network model in series.Then, it adopts particle swarm optimization algorithm to optimize weight and threshold of this combined model so as to get the improved grey neural network combined model for power forecasting in one day in advanced.Actual measuring data in one photovoltaic power station group verifies effectiveness of this forecasting method in improving forecasting precision.关键词
光伏短期预测/灰色模型/神经网络模型/平滑处理/粒子群算法Key words
photovoltaic short-term forecasting/grey model/neural network model/smoothing processing/particle swarm optimization algorithm分类
信息技术与安全科学引用本文复制引用
刘博洋,潘宇,许伯阳,刘文,李焕奇,王苏..基于改进灰色神经网络组合模型的光伏电站短期出力预测[J].广东电力,2017,30(4):55-60,6.
