Short-term Photovoltaic Power Prediction based on ICEEMDAN and Optimized Deep Hybrid Kernel Extreme Learning Machine


  • Hao Yan



Photovoltaic Power Generation, ICEEMDAN, Beluga Whale Optimization, Deep Hybrid Kernel Extreme Learning Machine


Aiming at the problem of low prediction accuracy for photovoltaic power generation due to the strong randomness and volatility, a prediction model based on Improved Complete Ensemble Empirical Mode Decomposition With Adaptive Noise (ICEEMDAN) and Beluga Whale Optimization Deep Hybrid Kernel Extreme Learning Machine (BWO-DHKELM) is proposed. Firstly, the historical data are analyzed by Pearson Correlation Coefficient, and the meteorological data with high correlation are obtained as the input features of the prediction model. Secondly, the PV power is decomposed by ICEEMDAN to reduce its volatility. Then, DHKELM is constructed for each subsequence and several parameters of the model are optimized by BWO. Finally, the predicted values of each subsequence are summed to obtain the final prediction results. The effectiveness and superiority of the proposed model are verified by using real data from a PV plant in Jiangsu, China as an example.


LAI Changwei, LI Jinghua, CHEN Bo, et al. Review of Photovoltaic Power Output Prediction Technology[J]. Transactions of China Electrotechnical Society,2019,34(06):1201-1217.

ZHANG Xiangying, YANG Yongbiao, XU Qingshan, et al. Photovoltaic Power Combination Prediction Method Based on Multi-Temporal Similarity Day Theory[J/OL]. Southern Power System Technology: 1-9[2023-03-16].

SHANG Liqun, LI Hongbo, HOU Yadong, et al. Short-term photovoltaic power generation prediction based on VMD-ISSA-KELM[J]. Power System Protection and Control, 2022,50(21):138-148.

LI Yi, YANG Mao, SU Xin. Short-Term Prediction of Photovoltaic Power Based on Integrated Clustering and Improved Markov Chain Model[J/OL]. Southern Power System Technology: 1-10[2023-03-16].

GONG Yingfei, LU Zongxiang, QIAO Ying, et al. An Overview of Photovoltaic Energy System Output Forecasting Technology[J]. Automation of Electric Power Systems, 2016, 40(4): 140-151.

ZHU Qiongfeng, LI Jiateng, QIAO Ji, et al. Application and Prospect of AI Technology in Renewable Energy Forecasting[J/OL]. Proceedings of the CSEE: 1-23[2023-03-16].

WANG Xin, HUANG Ke, ZHENG Yihui, et al. Short-term Forecasting Method of Photovoltaic Output Power Based on PNN/PCA/SS-SVR[J]. Automation of Electric Power Systems, 2016,40(17):156-162.

HUANG G B, WANG D H, LAN Y. Extreme learning machines: a survey[J]. International Journal of Machine Learning and Cybernetics, 2011, 2(2): 107-122.

Chu Y, Feng C, Guo C, et al. Network embedding based on deep extreme learning machine[J]. International Journal of Machine Learning and Cybernetics, 2019, 10(10): 2709-2724.

WU Chunhua, DONG Along, LIZhihua, et al. Photovoltaic Power Prediction Based on Graph Similarity Day and PSO-XGBoost[J]. High Voltage Engineering, 2022,48(08):3250-3259.

QIAO Weibiao, CHEN Baodong, WU Shijuan, et al. A forecasting model of natural gas daily load based on wavelet transform and LSSVM-DE[J]. Natural Gas Industry,2014,34(09):118-124.

YANG Haizhu, TIAN Fuming, ZHANG Peng, et al. Short-term load forecasting based on CEEMD-FE-AOA-LSSVM[J]. Power System Protection and Control, 2022,50(13):126-133.

Changting Zhong, Gang Li, Zeng Meng. Beluga whale optimization: A novel nature-inspired metaheuristic algorithm[J]. Knowledge-Based Systems,2022,251:109215.

Zhang Na, Ren Qiang, Liu Guangchen. et al. Short-term PV Output Power Forecasting Based on CEEMDAN-AE-GRU[J]. Journal of Electrical Engineering & Technology. 17, 1183–1194 (2022).

YANG Shuang, LUO Diansheng, HE Hongying, et al. Output power forecast of PV power system based on EMD-LSSVM model[J]. Acta Energiae Solaris Sinica, 2016,37(06):1387-1395.

WANG Zhenhao, WANG Chong, CHENG Long, et al. Photovoltaic Power Combined Prediction Based on Ensemble Empirical Mode Decomposition and Deep Learning[J]. High Voltage Engineering, 2022, 48(10):4133-4142.

WANG Rui, GAO Qiang, LU Jing. Short-term photovoltaic power prediction based on CEEMDAN-LSSVM-ARIMA model[J]. Transducer and Microsystem Technologies, 2022, 41(5): 118-122.

COLOMINAS M A, SCHLOTTHAUER G, TORRES M E. Improved complete ensemble EMD: a suitable tool for biomedical signal processing[J]. Biomed Signal Process Control, 2014, 14(1): 19-29.

Li Hongxia, Li Jianlin, Mi Yang. Research on Photovoltaic power prediction technology Based on Machine Learning[J]. Journal of Physics: Conference Series, 2021, 2087(1).

XIANG Ling, DENG Zeqi, ZHAO Yue. Multi-step Wind Speed Prediction Model Based on LPF-VMD and KELM [J]. Power System Technology, 2019,43(12):4461-4467.

WANG Rui, XU Xinchao, LU Jing. Short-term Wind Power Prediction Based on SSA Optimized Variational Mode Decomposition and Hybrid Kernel Extreme Learning Machine[J/OL]. Information and Control: 1-11[2023-03-17].

OUYANG Sen, SHI Yili. A new improved entropy method and its application in power quality evaluation[J]. Automation of Electric Power Systems, 2013, 37(21):156-159.

Khan J, Fayaz M, Hussain A, et al. An improved alpha beta filter using a deep extreme learning machine[J]. IEEE Access, 2021, 9: 61548-61564.







How to Cite

Yan, H. (2024). Short-term Photovoltaic Power Prediction based on ICEEMDAN and Optimized Deep Hybrid Kernel Extreme Learning Machine. International Journal of Mechanical and Electrical Engineering, 2(3), 32-46.

Similar Articles

1-10 of 27

You may also start an advanced similarity search for this article.