摘要
天气是影响光伏电站发电量的主要因素之一,天气的复杂性和随机性直接影响太阳辐射强度与稳定性,从而给光伏电站发电量短期预测带来极大的挑战,为此提出一种基于支持向量回归的光伏电站发电量短期预测方法。通过采集与预处理历史发电量数据及气象数据,构建适用于支持向量回归(SVR)模型的特征集;采用网格搜索与交叉验证技术优化SVR模型的参数,以确保模型的泛化能力;将预处理数据输入SVR模型中,得到预测结果。试验结果表明,与传统预测方法相比,基于SVR的预测方法在准确性和稳定性方面优势显著,预测误差较低,为光伏电站的运营管理提供了有力的技术支持。
Weather is one of the main factors affecting the power generation of photovoltaic power plants.The complexity and randomness of weather directly affect the intensity and stability of solar radiation,which poses great challenges to short-term prediction of photovoltaic power generation.Therefore,a support vector regression based short-term prediction method for photovoltaic power generation was proposed.A feature set suitable for support vector regression(SVR)models was constructed by collecting and preprocessing historical power generation and meteorological data.Grid search and cross validation techniques were used to optimize the parameters of the SVR model,ensuring its generalization ability.The preprocessed data was input into the SVR model to obtain relevant prediction results.The experimental results show that compared with traditional prediction methods,SVR based prediction methods have significant advantages in accuracy and stability,with lower prediction errors,and can provide strong technical support for the operation and management of photovoltaic power plants.
作者
程振飞
赵红伟
李朋
CHENG Zhenfei;ZHAO Hongwei;LI Peng(China Three Gorges New Energy(Group)Co.,Ltd.Shaanxi Branch,Xi′an 710076,China;Tongchuan Xiaguang New Energy Power Generation Co.,Ltd.,Tongchuan 727000,China)
出处
《电工材料》
2025年第4期110-113,共4页
Electrical Engineering Materials
关键词
光伏电站
发电量预测
支持向量回归
短期预测
数据预处理
模型优化
photovoltaic power station
power generation forecast
support vector regression
short term forecasting
data preprocessing
model optimization
