摘要
针对单一灰色模型和神经网络模型自身的缺陷对传统灰色神经网络组合模型预测精度的影响问题,提出一种基于改进的灰色神经网络组合模型的光伏电站短期出力预测方法。通过把历史日最高和最低气温以及功率数据作为输入,将改进后的单一灰色模型和神经网络模型进行串联组合。采用粒子群优化算法对该组合模型的权值和阈值进行优化,得到改进的灰色神经网络组合模型,可实现提前一天功率预测。某光伏电站群的实测数据验证了该预测方法能够有效提高预测精度。
In allusion t o the problem t ha t defec ts of the grey modei and t he neural ne t work modei have influences on forecas-ting precision of the traditionai grey neurai network combined model, this paper presents a method of photovoltaic power based on improved grey neural network modd. By taking the highest and the lowest tempera-ture and power data in history days as input, it combines the improved grey model andneural network model in series.Then, it adopts particle swarm optunization algorithm to optunize weight and threshold of this combined model so as to getthe improved grey neural network combined model for power one photovoltaic power station group verifies effectiveness of forecasting in one day in advanced. Actual measuring data in this forecas ting method in improving forecast ing precision.
出处
《广东电力》
2017年第4期55-60,共6页
Guangdong Electric Power
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
photovoltaic short-term forecasting
grey model
optimization algorithmneural network model
smoothing processing
particle swarm Key words： photovoltaic short-term forecasting
grey model
optimization algorithmneural network model
smoothing processing
particle swarm optimization algorithm
