A Two-Modal Weather Classification Method and Its Application in Photovoltaic Power Probability Prediction

doi:10.12096/j.2096-4528.pgt.23017

Abstract

Abstract:

Weather classification is an indispensable preprocessing step in photovoltaic (PV) power prediction. A new two-modal weather classification methods based on PV power clustering was proposed to finely depict the uncertainty of PV power output. Both PV power data and meteorological data were considered for weather classification, providing a novel and effective path for PV power prediction. In addition, data fusion technology was used to extract relevant information from both numeric weather prediction (NWP) data and measured meteorological data to help for weather classification. This approach reduces the model’s reliance on the accuracy of forecasted meteorological indicators and improve the robustness of the model. Experiments based on data from a PV power station in Jilin demonstrated the rationality of the proposed weather classification method. Combining the PV power probability prediction with the proposed weather classifier resulted in prediction interval coverage probabilities closer to the preassigned confidence level and narrower mean prediction interval width.

Key words: photovoltaic power generation, weather classification, photovoltaic power probability prediction, time series K-means clustering, multi-modal learning, uncertainty, numeric weather prediction

CLC Number:

TK 01

Xiaobiao FU, Jiaqi HOU, Baoju LI, Yakun WEN, Xiaowen LAI, Lei GUO, Zhiwei WANG, Yao WANG, Haifeng ZHANG, Dexin LI. A Two-Modal Weather Classification Method and Its Application in Photovoltaic Power Probability Prediction[J]. Power Generation Technology, 2024, 45(2): 299-311.

Figures/Tables 10

Fig. 1 Weather classification model based on the photovoltaic bimodal power clustering

Fig. 2 Photovoltaic power under different weather classifications

Fig. 3 Clustering results of photovoltaic power and corresponding irradiance and temperature

Fig. 4 Training process of photovoltaic bimodal weather classification model

Fig. 5 Framework of photovoltaic power conditional probability modeling and dayahead forecasting

Fig. 6 Scatter plot of actual power and predicted power of photovoltaic

Fig. 7 Photovoltaic power curves and confidence intervals

Tab. 1 Log-likelihood value of the Copula function

天气类型	Gaussian Copula	Student Copula	Clayton Copula	Frank Copula	Gumbel Copula
第0种	3 476.3	3 620.8	2 560.6	4 020.5	3 353.6
第1种	11 242.2	11 242.9	6 910.7	10 402.3	11 187.6
第2种	5 818.8	5 858.9	3 410.3	6 811.0	6 079.1

Fig. 8 Conditional probability distribution of photovoltaic power

Tab. 2 PICP and MPIW of weather classification under different confidence levels

类别	80%置信度		90%置信度		95%置信度
类别	P_ICP^α	M_PIW^α	P_ICP^α	M_PIW^α	P_ICP^α	M_PIW^α
天气分型	0.919 9	4.91	0.959 8	6.27	0.977 8	7.48
不进行天气分型	0.932 6	5.53	0.960 7	7.07	0.975 5	8.43

References 37

1	IEA ．World energy outlook 2017[R]．Paris：IEA，2017． doi:10.1787/g2659b7a2e-en
2	吴攀．光伏发电系统发电功率预测[J]．发电技术，2020，41(3)：231-236． doi:10.12096/j.2096-4528.pgt.19113
	WU P ．Power forecasting of photovoltaic power generation system[J]．Power Generation Technology，2020，41(3)：231-236． doi:10.12096/j.2096-4528.pgt.19113
3	吕清泉，张珍珍，马彦宏，等．区域光伏发电出力特性分析研究[J]．发电技术，2022，43(3)：413-420． doi:10.12096/j.2096-4528.pgt.21060
	LÜ Q Q， ZHANG Z Z， MA Y H，et al ．Analysis and research on output characteristics of regional photovoltaic power generation[J]．Power Generation Technology，2022，43(3)：413-420． doi:10.12096/j.2096-4528.pgt.21060
4	张思，杨晓雷，阙凌燕，等．高比例光伏发电对浙江电网电力平衡的影响及应对策略[J]．浙江电力，2022，41(11)：9-16．
	ZHANG S， YANG X L， QUE L Y，et al ．The impact of high-proportion photovoltaic power generation on the power balance of Zhejiang power grid and its countermeasures[J]．Zhejiang Electric Power，2022，41(11)：9-16．
5	刘宇，赵映，李世朝．光伏发电系统在火力发电厂的应用研究[J]．内蒙古电力技术，2022，40(2)：36-39．
	LIU Y， ZHAO Y， LI S Z ．Research on application of photovoltaic power generation system in thermal power plant[J]．Inner Mongolia Electric Power，2022，40(2)：36-39．
6	朱林，韩涛，董颖华，等．基于时变因素的光伏发电系统可靠性评估[J]．中国电力，2023，56(1)：158-165．
	ZHU L， HAN T， DONG Y H，et al ．Reliability evaluation of photovoltaic system based on time varying factors[J]．Electric Power，2023，56(1)：158-165．
7	王鹏翔，沈娟，王菁旸，等．基于PCA-LMD-WOA-ELM的短期光伏功率预测[J]．智慧电力，2022，50(6)：72-78． doi:10.3969/j.issn.1673-7598.2022.06.012
	WANG P X， SHEN J， WANG J Y，et al ．Short term photovoltaic power prediction based on PCA-LMD-WOA-ELM[J]．Smart Power，2022，50(6)：72-78． doi:10.3969/j.issn.1673-7598.2022.06.012
8	AHMED R， SREERAM V， MISHRA Y，et al ．A review and evaluation of the state-of-the-art in PV solar power forecasting：techniques and optimization[J]．Renewable and Sustainable Energy Reviews，2020，124：1364-0321． doi:10.1016/j.rser.2020.109792
9	国家能源局．光伏发电站功率预测系统技术要求：NB/T 32011—2013 [S]．北京：新华出版社，2014．
	National Energy Bureau of the People’s Republic of China ． Technical requirement of power forecasting system for PV power station：NB/T 32011—2013 [S]．Beijing：Xinhua Publishing House，2014．
10	赵唯嘉，张宁，康重庆，等．光伏发电出力的条件预测误差概率分布估计方法[J]．电力系统自动化，2015，39(16)：8-15． doi:10.1002/pip.1210
	ZHAO W J， ZHANG N， KANG C Q，et al ．A method of probabilistic distribution estimation of conditional forecast error for photovoltaic power generation[J]．Automation of Electric Power Systems，2015，39(16)：8-15． doi:10.1002/pip.1210
11	ISHII T， OTANI K， TAKASHIMA T，et al ．Solar spectral influence on the performance of photovoltaic (PV) modules under fine weather and cloudy weather conditions[J]．Progress in Photovoltaics：Research and Applications，2013，21(4)：481-489． doi:10.1002/pip.1210
12	WANG F， ZHANG Z， LIU C，et al ．Generative adversarial networks and convolutional neural networks based weather classification model for day ahead short-term photovoltaic power forecasting[J]．Energy Conversion and Management，2019，181：443-462． doi:10.1016/j.enconman.2018.11.074
13	WANG F， ZHEN Z， LIU C，et al ．Image phase shift invariance based cloud motion displacement vector calculation method for ultra-short-term solar PV power forecasting[J]．Energy Conversion and Management，2018，157：123-135． doi:10.1016/j.enconman.2017.11.080
14	叶林，裴铭，路朋，等．基于天气分型的短期光伏功率组合预测方法[J]．电力系统自动化，2021，45(1)：44-54． doi:10.7500/AEPS20200613003
	YE L， PEI M， LU P，et al ．Combination forecasting method of short-term photovoltaic power based on weather classification[J]．Automation of Electric Power Systems，2021，45(1)：44-54． doi:10.7500/AEPS20200613003
15	刘倩，胡强，杨凌帆，等．基于时间序列的深度学习光伏发电模型研究[J]．电力系统保护与控制，2021，49(19)：87-98． doi:10.19783/j.cnki.pspc.201494
	LIU Q， HU Q， YANG L F，et al ．Deep learning photovoltaic power generation model based on time series[J]．Power System Protection and Control，2021，49(19)：87-98． doi:10.19783/j.cnki.pspc.201494
16	陈向群，杨茂涛，刘谋海，等．基于模糊聚类分析的电能质量扰动模式识别方法[J]．电力科学与技术学报，2022，37(2)：79-85．
	CHEN X Q， YANG M T， LIU M H，et al ．Disturbance pattern recognition method of power quality based on the fuzzy clustering analysis[J]．Journal of Electric Power Science and Technology，2022，37(2)：79-85．
17	赵舫，盛青，王新刚，等．基于主成分分析合作博弈的电网负荷智能预测方法研究[J]．电网与清洁能源，2022，38(2)：48-52． doi:10.3969/j.issn.1674-3814.2022.02.007
	ZHAO F， SHENG Q， WANG X G，et al ．A study on the intelligent load forecasting method of power grids based on principal component analysis and cooperative game[J]．Power System and Clean Energy，2022，38(2)：48-52． doi:10.3969/j.issn.1674-3814.2022.02.007
18	王勃，冯双磊，刘纯．基于天气分型的风电功率预测方法[J]．电网技术，2014，38(1)：93-98． doi:10.13335/j.1000-3673.pst.2014.01.015
	WANG B， FENG S L， LIU C ．Study on weather typing based wind power prediction[J]．Power System Technology，2014，38(1)：93-98． doi:10.13335/j.1000-3673.pst.2014.01.015
19	CHEN C， DUAN S， CAI T，et al ．Online 24-h solar power forecasting based on weather type classification using artificial neural network[J]．Solar Energy，2011，85(11)：2856-2870． doi:10.1016/j.solener.2011.08.027
20	李芬，周尔畅，孙改平，等．一种新型天气分型方法及其在光伏功率预测中的应用[J]．上海交通大学学报，2021，55(12)：1510-1519．
	LI F， ZHOU E C， SUN G P，et al ．A novel weather classification method and its application in photovoltaic power prediction[J]．Journal of Shanghai Jiao Tong University，2021，55(12)：1510-1519．
21	AGHABOZORGI S， SEYED SHIRKHORSHIDI A， YING WAH T ．Time-series clustering：a decade review[J]．Information Systems，2015，53：16-38． doi:10.1016/j.is.2015.04.007
22	BENGIO Y， COURVILLE A， VINCENT P ．Representation learning：a review and new perspectives[J]．IEEE Transactions on Pattern Analysis and Machine Intelligence，2013，35(8)：1798-1828． doi:10.1109/tpami.2013.50
23	赵亮．多模态数据融合算法研究[D]．大连：大连理工大学，2018．
	ZHAO L ．Research on multimodal data fusion methods[D]．Dalian：Dalian University of Technology，2018．
24	乔延辉，韩爽，许彦平，等．基于天气分型的风光出力互补性分析方法[J]．电力系统自动化，2021，45(2)：82-88． doi:10.7500/AEPS20200812006
	QIAO Y H， HAN S， XU Y P，et al ．Analysis method for complementarity between wind and photovoltaic power outputs based on weather classification[J]．Automation of Electric Power Systems，2021，45(2)：82-88． doi:10.7500/AEPS20200812006
25	管霖，赵琦，周保荣，等．基于多尺度聚类分析的光伏功率特性建模及预测应用[J]．电力系统自动化，2018，42(15)：24-30． doi:10.7500/AEPS20171027003
	GUAN L， ZHAO Q， ZHOU B R，et al ．Multi-scale clustering analysis based modeling of photovoltaic power characteristics and its application in prediction[J]．Automation of Electric Power Systems，2018，42(15)：24-30． doi:10.7500/AEPS20171027003
26	BALTRUSAITIS T， AHUJA C， MORENCY L P ．Multimodal machine learning：a survey and taxonomy[J]．IEEE Transactions on Pattern Analysis and Machine Intelligence，2019，41(2)：423-443． doi:10.1109/tpami.2018.2798607
27	朱乔木．基于深度学习的电力系统暂态稳定评估及风电功率预测方法研究[D]．武汉：华中科技大学，2019．
	ZHU Q M ．Research on power system transient stability assessment and wind power prediction method based on deep learning[D]．Wuhan：Huazhong University of Science and Technology，2019．
28	师浩琪，郭力，刘一欣，等．基于多源气象预报总辐照度修正的光伏功率短期预测[J]．电力自动化设备，2022，42(3)：104-112．
	SHI H Q， GUO L， LIU Y X，et al ．Short-term forecasting of photovoltaic power based on total irradiance correction of multi-source meteorological forecast[J]．Electric Power Automation Equipment，2022，42(3)：104-112．
29	张永蕊，阎洁，林爱美，等．多点数值天气预报风速和辐照度集中式修正方法研究[J]．发电技术，2022，43(2)：278-286． doi:10.12096/j.2096-4528.pgt.22005
	ZHANG Y R， YAN J， LIN A M，et al ．Integrated correction method of multi-point numerical weather prediction wind speed and irradiance[J]．Power Generation Technology，2022，43(2)：278-286． doi:10.12096/j.2096-4528.pgt.22005
30	COWLEY B R， SEMEDO J D， ZANDVAKILI A，et al ．Distance covariance analysis[C]//International Conference on Artificial Intelligence and Statistics， PMLR，2017，54：242-251．
31	MATTESON D S， TSAY R S ．Independent component analysis via distance covariance[J]．Journal of the American Statistical Association，2017，112(518)：623-637． doi:10.1080/01621459.2016.1150851
32	HUO X， SZÉKELY G J ．Fast computing for distance covariance[J]．Technometrics，2016，58(4)：435-447． doi:10.1080/00401706.2015.1054435
33	ZHANG N， KANG C， XU Q，et al ．Modelling and simulating the spatio-temporal correlations of clustered wind power using copula[J]．Journal of Electrical Engineering and Technology，2013，8(6)：1615-1625． doi:10.5370/jeet.2013.8.6.1615
34	WĘGLARCZYK S ．Kernel density estimation and its application[J]．ITM Web of Conferences，2018，23：00037． doi:10.1051/itmconf/20182300037
35	DURANTE F， SEMPI C ．Copula theory：an introduction[C]//Copula Theory and Its Applications，Lecture Notes in Statistics．Berlin，Heidelberg：Springer，2010：3-31． doi:10.1007/978-3-642-12465-5_1
36	KHOSRAVI A， NAHAVANDI S， CREIGHTON D ．Construction of optimal prediction intervals for load forecasting problems[J]．IEEE Transactions on Power Systems，2010，25(3)：1496-1503． doi:10.1109/tpwrs.2010.2042309
37	KHOSRAVI A， NAHAVANDI S， CREIGHTON D，et al ．Comprehensive review of neural network-based prediction intervals and new advances[J]．IEEE Transactions on Neural Networks，2011，22(9)：1341-1356． doi:10.1109/tnn.2011.2162110

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