DATA MINING METHOD FOR PHOTOVOLTAIC POWER PREDICTION BASED ON RFI AND PFE

Gao Letian; Gu Wenbo

doi:10.19912/j.0254-0096.tynxb.2023-2020

PDF(1150 KB)

Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (4) : 256-262. DOI: 10.19912/j.0254-0096.tynxb.2023-2020

DATA MINING METHOD FOR PHOTOVOLTAIC POWER PREDICTION BASED ON RFI AND PFE

Gao Letian, Gu Wenbo

Author information +

History +

Abstract

To mitigate the negative impacts on photovoltaic power generation predictions from time-varying and non-existent features within datasets, such as the lifespan and cleanliness of photovoltaic panels within datasets, this paper introduces a data mining method tailored for small sample sizes that leverages random forest feature importance ranking and polynomial feature expansion. Initially, the method ranks features according to their importance as determined by the random forest algorithm. Subsequently, it identifies the optimal number of features to retain and determines the most appropriate degree of polynomial feature expansion for the regression model. The method then compares prediction results for both the cross-validation and test sets before and after applying dimensionality enhancement. The findings indicate that the data mining approach proposed in this study significantly enhances the prediction accuracy of the multilayer perceptron regressor （MLPR） model and three time series regression models derived from MLPR: recurrent neural network （RNN）, gated recurrent unit （GRU）, and long short-term memory （LSTM） networks, particularly in scenarios involving small sample sizes.

Key words

data mining / PV power generation / forecasting / small sample / random forest importance sorting / polynomial feature expansion / cross-validation

Cite this article

EndNote

Ris (Procite)

Bibtex

Download Citations

Gao Letian, Gu Wenbo. DATA MINING METHOD FOR PHOTOVOLTAIC POWER PREDICTION BASED ON RFI AND PFE[J]. Acta Energiae Solaris Sinica. 2025, 46(4): 256-262 https://doi.org/10.19912/j.0254-0096.tynxb.2023-2020

References

[1] BP. Statistical review of world energy[EB/OL].https://www.bp.com/.
[2] 陈元峰, 马溪原, 程凯, 等. 基于气象特征量选取与SVM模型参数优化的新能源超短期功率预测[J]. 太阳能学报, 2023, 44(12): 568-576.
CHEN Y F, MA X Y, CHENG K, et al.Ultra-short-term power forecast of new energy based on meteorological feature selection and SVM model parameter optimization[J]. Acta energiae solaris sinica, 2023, 44(12): 568-576.
[3] 麻吕斌, 潘国兵, 蒋群, 等. 基于EOF-DBSCAN-GRU的分布式光伏集群出力预测方法研究[J]. 太阳能学报, 2024, 45(1): 39-46.
MA L B, PAN G B, JIANG Q, et al.Research on distributed PV cluster power output forecasting method based on EOF-DBSCAN-GRU[J]. Acta energiae solaris sinica, 2024, 45(1): 39-46.
[4] 王超, 蔺红, 庞晓虹. 基于HPO-VMD和MISMA-DHKELM的短期光伏功率组合预测[J]. 太阳能学报, 2023, 44(12): 65-73.
WANG C, LIN H, PANG X H.Short-term photovoltaic power combination prediction based on HPO-VMD and MISMA-DHKELM[J]. Acta energiae solaris sinica, 2023, 44(12): 65-73.
[5] 朱琼锋, 李家腾, 乔骥, 等. 人工智能技术在新能源功率预测的应用及展望[J]. 中国电机工程学报, 2023, 43(8): 3027-3048.
ZHU Q F, LI J T, QIAO J, et al.Application and prospect of artificial intelligence technology in renewable energy forecasting[J]. Proceedings of the CSEE, 2023, 43(8): 3027-3048.
[6] 张成, 白建波, 兰康, 等. 基于数据挖掘和遗传小波神经网络的光伏电站发电量预测[J]. 太阳能学报, 2021, 42(3): 375-382.
ZHANG C, BAI J B, LAN K, et al.Photovoltaic power generation prediction based on data mining and genetic wavelet neural network[J]. Acta energiae solaris sinica, 2021, 42(3): 375-382.
[7] 王涛, 王旭, 许野, 等. 计及相似日的LSTM光伏出力预测模型研究[J]. 太阳能学报, 2023, 44(8): 316-323.
WANG T, WANG X, XU Y, et al.Study on LSTM photovoltaic output prediction model considering similar days[J]. Acta energiae solaris sinica, 2023, 44(8): 316-323.
[8] 董雪, 赵宏伟, 赵生校, 等. 基于SOM聚类和二次分解的BiGRU超短期光伏功率预测[J]. 太阳能学报, 2022, 43(11): 85-93.
DONG X, ZHAO H W, ZHAO S X, et al.Ultra-short-term forecasting method of photovoltaic power based on SOM clustering, secondary decomposition and BiGRU[J]. Acta energiae solaris sinica, 2022, 43(11): 85-93.
[9] 宋煜, 郭军红, 袁荔, 等. 基于逐步聚类分析的短期光伏发电预测方法[J]. 热能动力工程, 2023, 38(10): 158-166.
SONG Y, GUO J H, YUAN L, et al.Short-term photovoltaic power generation prediction method based on stepwise clustering analysis[J]. Journal of engineering for thermal energy and power, 2023, 38(10): 158-166.
[10] 吕伟杰, 方一帆, 程泽. 基于模糊C均值聚类和样本加权卷积神经网络的日前光伏出力预测研究[J]. 电网技术, 2022, 46(1): 231-238.
LYU W J, FANG Y F, CHENG Z.Prediction of day-ahead photovoltaic output based on FCM-WS-CNN[J]. Power system technology, 2022, 46(1): 231-238.
[11] 孟安波, 陈嘉铭, 黎湛联, 等. 基于相似日理论和CSO-WGPR的短期光伏发电功率预测[J]. 高电压技术, 2021, 47(4): 1176-1184.
MENG A B, CHEN J M, LI Z L, et al.Short-term photovoltaic power generation prediction based on similar day theory and CSO-WGPR[J]. High voltage engineering, 2021, 47(4): 1176-1184.
[12] 武新章, 王泽宇, 代伟, 等. 基于异质聚类与Stacking的双集成光伏发电功率预测[J]. 电网技术, 2023, 47(1): 275-284.
WU X Z, WANG Z Y, DAI W, et al.Bi-ensembled photovoltaic(PV) power prediction based on heterogeneous clustering and stacking[J]. Power system technology, 2023, 47(1): 275-284.
[13] 孟安波, 许炫淙, 陈嘉铭, 等. 基于强化学习和组合式深度学习模型的超短期光伏功率预测[J]. 电网技术, 2021, 45(12): 4721-4728.
MENG A B, XU X C, CHEN J M, et al.Ultra short term photovoltaic power prediction based on reinforcement learning and combined deep learning model[J]. Power system technology, 2021, 45(12): 4721-4728.
[14] 袁建华, 谢斌斌, 何宝林, 等. 基于DTW-VMD-PSO-BP的光伏发电功率短期预测方法[J]. 太阳能学报, 2022, 43(8): 58-66.
YUAN J H, XIE B B, HE B L, et al.Short term forecasting method of photovoltaic output based on DTW-VMD-PSO-BP[J]. Acta energiae solaris sinica, 2022, 43(8): 58-66.
[15] 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.
[16] 卿会, 李薇, 刘文娇, 等. 基于极点对称模态分解-支持向量机的短期光伏发电预测方法[J]. 热能动力工程, 2022, 37(10): 189-197.
QING H, LI W, LIU W J, et al.Research on short-term photovoltaic power forecasting technology based on ESMD-SVM[J]. Journal of engineering for thermal energy and power, 2022, 37(10): 189-197.
[17] 王伟胜, 车建峰, 王勃, 等. 光伏发电功率预测技术及应用[M]. 北京: 中国电力出版社, 2019: 19.
WANG W S, CHE J F, WANG B, et al.Photovoltaic power prediction technology and its application[M]. Beijing: China Electric Power Press, 2019: 19.
[18] 牛海明, 崔青汝, 刘厚旭. 积灰对光伏电池板输出特性影响研究[J]. 热力发电, 2021, 50(2): 110-117.
NIU H M, CUI Q R, LIU H X.Effect of ash accumulation on output performance of photovoltaic panels[J]. Thermal power generation, 2021, 50(2): 110-117.
[19] 陈宇轩, 张耀, 徐杨, 等. 基于Boosting集成框架的新能源发电功率异常值检测方法[J]. 电网技术, 2023, 47(8): 3261-3268.
CHEN Y X, ZHANG Y, XU Y, et al.Outlier detection method of new energy power based on boosting integration framework[J]. Power system technology, 2023, 47(8): 3261-3268.
[20] LEE W M.Python machine learning[M]. Birmingham:Packt Publishing: 2015.