为了提高模型预测性能,提出一种综合太阳辐射模型及深度学习的光伏功率预测模型。首先,利用太阳辐射机理建立太阳辐射模型（SRM）,估算出水平面上总辐射值,再由斜面辐照度转换方法计算出光伏组件所接收的斜面辐射值。其次,通过皮尔逊相关分析法筛选出对光伏功率影响较大的主要因素,将斜面辐射计算值及主要影响因素作为输入,采用卷积神经网络（CNN）和长短期记忆网络（LSTM）建立光伏功率SRM-CNN-LSTM预测模型。分别利用春夏秋冬四季典型日的数据开展对比实验,结果表明：与几种其他方法相比,该文方法具有更好的预测效果。
Abstract
Precise photovoltaic power forecast is helpful for grid dispatching and secure operation. To enhance the forecast performance of the model, a PV prediction model combining the solar radiation model and deep learning is suggested. Firstly, the solar radiation model (SRM) is built using the solar radiation mechanism to estimate the total radiation value on the horizontal plane. Then the inclined plane radiation value received by the inclined photovoltaic panel is calculated by the inclined plane irradiance conversion method. Secondly, Pearson correlation analysis is devoted to screen out the primary factors influencing greatly photovoltaic power. Finally, the calculated value of inclined plane radiation and the major influencing factors are taken as input and derived from convolutional neural network （CNN） and long short-term memory（LSTM） network to build the PV power SRM-CNN-LSTM prediction model. Comparative experiments are carried out with the data from typical spring, summer, autumn, and winter days. The results show that the suggested method has better forecast effect compared with several other methods.
关键词
光伏发电 /
预测 /
太阳辐射 /
神经网络 /
卷积神经网络 /
长短期记忆网络
Key words
photovoltaic power /
forecasting /
solar radiation /
neural network /
convolutional neural network /
long short-term memory network
{{custom_sec.title}}
{{custom_sec.title}}
{{custom_sec.content}}
参考文献
[1] 王开艳, 杜浩东, 贾嵘, 等. 基于相似日聚类和QR-CNN-BiLSTM模型的光伏功率短期区间概率预测[J]. 高电压技术, 2022, 48(11): 4372-4388.
WANG K Y, DU H D, JIA R, et al.Short-term interval probability prediction of photovoltaic power based on similar daily clustering and QR-CNN-BiLSTM model[J]. High voltage engineering, 2022, 48(11): 4372-4388.
[2] 张雲钦, 程起泽, 蒋文杰, 等. 基于EMD-PCA-LSTM的光伏功率预测模型[J]. 太阳能学报, 2021, 42(9): 62-69.
ZHANG Y Q, CHENG Q Z, JIANG W J, et al.Photovoltaic power prediction model based on EMD-PCA-LSTM[J]. Acta energiae solaris sinica, 2021, 42(9): 62-69.
[3] 吉锌格, 李慧, 叶林, 等. 基于波动特性挖掘的短期光伏功率预测[J]. 太阳能学报, 2022, 43(5): 146-155.
JI X G, LI H, YE L, et al.Short-term photovoltaic power forecasting based on fluctuation characteristics mining[J]. Acta energiae solaris sinica, 2022, 43(5): 146-155.
[4] HUANG X Q, LI Q, TAI Y H, et al.Hybrid deep neural model for hourly solar irradiance forecasting[J]. Renewable energy, 2021, 171: 1041-1060.
[5] DAS U K, TEY K S, SEYEDMAHMOUDIAN M, et al.Forecasting of photovoltaic power generation and model optimization: a review[J]. Renewable and sustainable energy reviews, 2018, 81: 912-928.
[6] 刘兴冉, 张宏晔, 闫海明, 等. 区域复杂地形晴日太阳辐射估算研究[J]. 太阳能学报, 2022, 43(8): 174-180.
LIU X R, ZHANG H Y, YAN H M, et al.Estimation of regional solar radiation on clear days over complex terrian[J]. Acta energiae solaris sinica, 2022, 43(8): 174-180.
[7] 姜文玲, 赵艳青, 王勃, 等. 基于NWP辐照度斜面转换的光伏功率预测方法[J]. 山东大学学报(工学版), 2021, 51(5): 114-121.
JIANG W L, ZHAO Y Q, WANG B, et al.Photovoltaic power prediction method based on NWP irradiance inclination conversion[J]. Journal of Shandong University (engineering science), 2021, 51(5): 114-121.
[8] 王俊杰, 毕利, 张凯, 等. 基于多特征融合和XGBoost-LightGBM-ConvLSTM的短期光伏发电量预测[J]. 太阳能学报, 2023, 44(7): 168-174.
WANG J J, BI L, ZHANG K, et al.Short-term photovoltaic power generation prediction based on multi-feature fusion and XGBoost-LightGBM-ConvLSTM[J]. Acta energiae solaris sinica, 2023, 44(7): 168-174.
[9] 冯裕祺, 李辉, 李利娟, 等. 基于CNN-GRU的光伏电站电压轨迹预测[J]. 中国电力, 2022, 55(7): 163-171.
FENG Y Q, LI H, LI L J, et al.Voltage trajectory prediction of photovoltaic power station based on CNN-GRU[J]. Electric power, 2022, 55(7): 163-171.
[10] YOUSSEF A, EL-TELBANY M, ZEKRY A.The role of artificial intelligence in photo-voltaic systems design and control: a review[J]. Renewable and sustainable energy reviews, 2017, 78: 72-79.
[11] GAO B X, HUANG X Q, SHI J S, et al.Hourly forecasting of solar irradiance based on CEEMDAN and multi-strategy CNN-LSTM neural networks[J]. Renewable energy, 2020, 162: 1665-1683.
[12] 王福忠, 王帅峰, 张丽. 基于VMD-LSTM与误差补偿的光伏发电超短期功率预测[J]. 太阳能学报, 2022, 43(8): 96-103.
WANG F Z, WANG S F, ZHANG L.Ultra short term power prediction of photovoltaic power generation based on VMD-LSTM and error compensation[J]. Acta energiae solaris sinica, 2022, 43(8): 96-103.
[13] 史凯钰, 张东霞, 韩肖清, 等. 基于LSTM与迁移学习的光伏发电功率预测数字孪生模型[J]. 电网技术, 2022, 46(4): 1363-1372.
SHI K Y, ZHANG D X, HAN X Q, et al.Digital twin model of photovoltaic power generation prediction based on LSTM and transfer learning[J]. Power system technology, 2022, 46(4): 1363-1372.
[14] 于瑛, 贾晓宇, 陈笑. 基于气候突变年的太阳辐射模型统计时长选取方法[J]. 哈尔滨工业大学学报, 2021, 53(1): 193-200.
YU Y, JIA X Y, CHEN X.Selection method of statistical duration in solar radiation model based on climate abrupt change year[J]. Journal of Harbin Institute of Technology, 2021, 53(1): 193-200.
[15] CAMPBELL G S, NORMAN J M.An introduction to environmental biophysics[J]. Biologia plantarum, 1979, 21(2): 104.
[16] LIU B Y H, JORDAN R C. The interrelationship and characteristic distribution of direct, diffuse and total solar radiation[J]. Solar energy, 1960, 4(3): 1-19.
基金
中央高校基本科研业务费专项资金（2021MS089）
PDF(1785 KB)
