摘要
通过实测光伏电站所在区域主要气象参数和光伏电站电气参数,应用BP神经网络算法建立光伏电站数学模型,形成预测样本数据库,不断与历史数据、历史曲线、历史预测结果进行对比与改进,以实现较小的误差预测。光伏电站光功率及发电量预测,有利于提高电网接纳光伏发电的能力,促进电网对不稳定可再生能源的接纳和消化。
Through measuring main meteorologic parameters and photovoltaic power station electricity parameters of station site region,the paper establishes photovoltaic power station mathematical model using BP neural network algorithm,and forms forecast sample database,thus makes comparison and improvement with historical data,curve and forecast result,in order to realize lesser error forecast,the forecast of optical power and power generation of photovoltaic power station is propitious to improve the capability of power grid adopting the photovoltaic generation,and it will promote power grid adoption and digestion ability on unstable and renewable energy.
出处
《青海电力》
2010年第2期18-20,共3页
Qinghai Electric Power
关键词
并网光伏电站
光功率
预测
方法
shunt-connected photovoltaic power station
optical power
forecast
method
