Journal of Arid Meteorology ›› 2020, Vol. 38 ›› Issue (5): 869-877.DOI: 10.11755/j.issn.1006-7639(2020)-05-0869

Previous Articles     Next Articles

Prediction Methods of Short-term Photovoltaic Power Based on Inclined Plane Solar Radiation Algorithm

LI Yao1, LI Zhaorong2, WANG Xiaoyong1, YAN Xiaomin1, ZHAO Wenjing1   

  1. 1. Gansu Provincial Meteorological Service Center, Lanzhou 730020, China;
    2. Gansu Provincial Meteorological Bureau, Lanzhou 730020, China

  • Online:2020-10-30 Published:2020-10-30

基于斜面辐射算法的短期光伏功率预测方法研究

李遥1,李照荣2,王小勇1,闫晓敏1,赵文婧1   

  1. 1.甘肃省气象服务中心,甘肃 兰州 730020;
    2.甘肃省气象局,甘肃 兰州 730020
  • 作者简介:李遥(1989— ),女,硕士,工程师,主要从事新能源功率预测服务工作.
  • 基金资助:
    甘肃省气象局成果转化项目“光电物理转换模型的升级与应用”(GSMACg2016-19)和甘肃省气象局创新团队(GSQXCXTD-2020-03)共同资助

Abstract: Based on observation data and numerical forecast data at ZDLYFP photovoltaic power station from March 2017 to February 2019, the inclined plane total solar radiation algorithm was improved, firstly. And on this basis two forecast models of short-term photovoltaic output power were established by using multiple linear regression (MLR) and empirical formula methods, then the forecast results were tested and evaluated. The results are as follows: (1) The inclined plane total solar radiation and temperature were higher correlated with photovoltaic output power in each season, the total correlation coefficients were 0.896 and 0.386, respectively, so they were introduced to forecast model based on MLR method as the predictors of photovoltaic output power. (2) The forecast effect of short-term power improved after the improvement of inclined plane solar radiation algorithm, and the relative root mean square error (RRMSE) of photovoltaic output power forecasted by two models of MLR and empirical formula methods reduced by 0.066 and 0.040, respectively. (3) The total root mean square error (RMSE) of photovoltaic output power obtained by MLR and empirical formula methods were 940.917 kW and 1147.172 kW, respectively, and total RRMSEs were 0.188 and 0.229. In addition, RMSEs and RRMSEs based on MLR method were less than those based on empirical formula method in each month, and the correlation coefficient of the former was slightly higher than that of the latter, which indicated that the forecast effect of MLR method was better and more stable in practical application. (4) The effect of photovoltaic power prediction was obviously distinct under different weather conditions, RRMSEs of two methods increased in turn for sunny weather, cloudy weather, overcast weather, rainy weather, dust weather and snow weather. In general, the effect of photovoltaic power prediction based on MLR method was better under different weather conditions.

Key words: photovoltaic power prediction, inclined plane solar radiation, MLR method, empirical formula method

摘要: 基于2017年3月至2019年2月中电芦阳扶贫光伏电站观测数据和数值预报资料,在斜面辐射算法改进的基础上,利用多元线性回归法和经验公式法建立适用于本地电站的两种短期光伏功率预测模型,并对预报效果进行检验和评估。结果表明:(1)各季节斜面总辐射和温度与电站光伏发电功率相关性较高,总相关系数分别为0.896和0.386,可作为发电功率的预报因子引入多元线性回归预报模型;(2)斜面辐射算法改进后,短期光伏功率预测效果有所提高,多元线性回归法和经验公式法的相对均方根误差(RRMSE)分别降低了0.066和0.040;(3)多元线性回归法和经验公式法总的均方根误差(RMSE)分别为940.917 kW和1147.172 kW,RRMSE分别为0.188和0.229,且多元线性回归法各月RMSE和RRMSE均小于经验公式法,相关系数略高于经验公式法,预报效果更好、更稳定;(4)不同天气条件下功率预测效果差异明显,两种方法的RRMSE值均自晴天、多云天、阴天、雨天、沙尘天、雪天依次增大,总体来说,各种天气条件下多元线性回归法的功率预测效果更优。

关键词: 光伏功率预测, 斜面辐射, 多元线性回归法, 经验公式法

CLC Number: