• CN 62-1175/P
• ISSN 1006-7639
• 双月刊
• 中国科技核心期刊
• 中国学术期刊综合评价数据库统计源期刊
• 中文科技期刊数据库收录期刊

• 业务技术应用 •

### 基于斜面辐射算法的短期光伏功率预测方法研究

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
• 出版日期:2020-10-30 发布日期:2020-10-30
• 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

### 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

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.

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