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A hybrid PV cluster power prediction model using BLS with GMCC and error correction via RVM considering an improved statistical upscaling technique

Author

Listed:
  • Qiu, Lihong
  • Ma, Wentao
  • Feng, Xiaoyang
  • Dai, Jiahui
  • Dong, Yuzhuo
  • Duan, Jiandong
  • Chen, Badong

Abstract

Accurate cluster photovoltaic power prediction (CPPP) is crucial for the operation and control of renewable energy grid-connected power systems. The traditional modeling strategies for CPPP such as direct aggregation (DA) and statistical upscaling (SU) have limitations such as error accumulation and upscaling factor uncertainty. To address these issues, this paper proposed a novel hybrid approach for CPPP by combining machine learning models with an improved SU technique. Firstly, a robust broad learning system (BLS) model, in which the Generalized Maximum Correntropy Criterion (GMCC) is used to replace the original mean square error (MSE) loss in BLS, is proposed to solve the problem of multiple outliers affecting the prediction accuracy of regional cluster stations, and it is called GBLS. Then, the Relevance Vector Machine (RVM) as an effective nonlinear regression model is further utilized to compensate for the prediction errors obtained by the GBLS to form the hybrid prediction model, namely GBLS-RVM. Moreover, to mitigate the uncertainty associated with scaling factors in traditional SU strategy, a new SU strategy is developed to refine the relationship between the reference station and the cluster sub-region, enabling direct modeling for regional power prediction. Finally, data from two PV clusters in different regions of China are used to validate the effectiveness of the proposed model, and the results show that under the improved SU strategy, the GBLS-RVM model, reduced RMSE by approximately 33.7% compared to the traditional BLS model, and the RMSE decreased by 12.89% and 30.2% when compared to traditional DA and traditional SU strategies.

Suggested Citation

  • Qiu, Lihong & Ma, Wentao & Feng, Xiaoyang & Dai, Jiahui & Dong, Yuzhuo & Duan, Jiandong & Chen, Badong, 2024. "A hybrid PV cluster power prediction model using BLS with GMCC and error correction via RVM considering an improved statistical upscaling technique," Applied Energy, Elsevier, vol. 359(C).
    • Handle: RePEc:eee:appene:v:359:y:2024:i:c:s0306261924001028
    DOI: 10.1016/j.apenergy.2024.122719
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    as
    1. Ramadhan, Raden A.A. & Heatubun, Yosca R.J. & Tan, Sek F. & Lee, Hyun-Jin, 2021. "Comparison of physical and machine learning models for estimating solar irradiance and photovoltaic power," Renewable Energy, Elsevier, vol. 178(C), pages 1006-1019.
    2. Xu, Xuefang & Hu, Shiting & Shi, Peiming & Shao, Huaishuang & Li, Ruixiong & Li, Zhi, 2023. "Natural phase space reconstruction-based broad learning system for short-term wind speed prediction: Case studies of an offshore wind farm," Energy, Elsevier, vol. 262(PA).
    3. Li, Pengtao & Zhou, Kaile & Lu, Xinhui & Yang, Shanlin, 2020. "A hybrid deep learning model for short-term PV power forecasting," Applied Energy, Elsevier, vol. 259(C).
    4. Yang, Dazhi & Wang, Wenting & Gueymard, Christian A. & Hong, Tao & Kleissl, Jan & Huang, Jing & Perez, Marc J. & Perez, Richard & Bright, Jamie M. & Xia, Xiang’ao & van der Meer, Dennis & Peters, Ian , 2022. "A review of solar forecasting, its dependence on atmospheric sciences and implications for grid integration: Towards carbon neutrality," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    5. Chen, Wenhe & Zhou, Hanting & Cheng, Longsheng & Xia, Min, 2023. "Prediction of regional wind power generation using a multi-objective optimized deep learning model with temporal pattern attention," Energy, Elsevier, vol. 278(PB).
    6. Shaker, Hamid & Manfre, Daniel & Zareipour, Hamidreza, 2020. "Forecasting the aggregated output of a large fleet of small behind-the-meter solar photovoltaic sites," Renewable Energy, Elsevier, vol. 147(P1), pages 1861-1869.
    7. Brester, Christina & Kallio-Myers, Viivi & Lindfors, Anders V. & Kolehmainen, Mikko & Niska, Harri, 2023. "Evaluating neural network models in site-specific solar PV forecasting using numerical weather prediction data and weather observations," Renewable Energy, Elsevier, vol. 207(C), pages 266-274.
    8. Pierro, Marco & Gentili, Damiano & Liolli, Fabio Romano & Cornaro, Cristina & Moser, David & Betti, Alessandro & Moschella, Michela & Collino, Elena & Ronzio, Dario & van der Meer, Dennis, 2022. "Progress in regional PV power forecasting: A sensitivity analysis on the Italian case study," Renewable Energy, Elsevier, vol. 189(C), pages 983-996.
    9. Ye, Lin & Dai, Binhua & Li, Zhuo & Pei, Ming & Zhao, Yongning & Lu, Peng, 2022. "An ensemble method for short-term wind power prediction considering error correction strategy," Applied Energy, Elsevier, vol. 322(C).
    10. Duan, Jiandong & Wang, Peng & Ma, Wentao & Tian, Xuan & Fang, Shuai & Cheng, Yulin & Chang, Ying & Liu, Haofan, 2021. "Short-term wind power forecasting using the hybrid model of improved variational mode decomposition and Correntropy Long Short -term memory neural network," Energy, Elsevier, vol. 214(C).
    11. Yu, Guangzheng & Liu, Chengquan & Tang, Bo & Chen, Rusi & Lu, Liu & Cui, Chaoyue & Hu, Yue & Shen, Lingxu & Muyeen, S.M., 2022. "Short term wind power prediction for regional wind farms based on spatial-temporal characteristic distribution," Renewable Energy, Elsevier, vol. 199(C), pages 599-612.
    12. Wen, Lulu & Zhou, Kaile & Yang, Shanlin & Lu, Xinhui, 2019. "Optimal load dispatch of community microgrid with deep learning based solar power and load forecasting," Energy, Elsevier, vol. 171(C), pages 1053-1065.
    13. Wang, Fei & Lu, Xiaoxing & Mei, Shengwei & Su, Ying & Zhen, Zhao & Zou, Zubing & Zhang, Xuemin & Yin, Rui & Duić, Neven & Shafie-khah, Miadreza & Catalão, João P.S., 2022. "A satellite image data based ultra-short-term solar PV power forecasting method considering cloud information from neighboring plant," Energy, Elsevier, vol. 238(PC).
    14. Zhang, Meng & Hu, Tao & Wu, Lifeng & Kang, Guoqing & Guan, Yong, 2021. "A method for capacity estimation of lithium-ion batteries based on adaptive time-shifting broad learning system," Energy, Elsevier, vol. 231(C).
    15. Bisoi, Ranjeeta & Dash, Deepak Ranjan & Dash, P.K. & Tripathy, Lokanath, 2022. "An efficient robust optimized functional link broad learning system for solar irradiance prediction," Applied Energy, Elsevier, vol. 319(C).
    16. Theocharides, Spyros & Makrides, George & Livera, Andreas & Theristis, Marios & Kaimakis, Paris & Georghiou, George E., 2020. "Day-ahead photovoltaic power production forecasting methodology based on machine learning and statistical post-processing," Applied Energy, Elsevier, vol. 268(C).
    17. Simeunović, Jelena & Schubnel, Baptiste & Alet, Pierre-Jean & Carrillo, Rafael E. & Frossard, Pascal, 2022. "Interpretable temporal-spatial graph attention network for multi-site PV power forecasting," Applied Energy, Elsevier, vol. 327(C).
    18. Nguyen, Thi Ngoc & Müsgens, Felix, 2022. "What drives the accuracy of PV output forecasts?," Applied Energy, Elsevier, vol. 323(C).
    19. Scott, Connor & Ahsan, Mominul & Albarbar, Alhussein, 2023. "Machine learning for forecasting a photovoltaic (PV) generation system," Energy, Elsevier, vol. 278(C).
    20. Liu, Chenyu & Zhang, Xuemin & Mei, Shengwei & Zhen, Zhao & Jia, Mengshuo & Li, Zheng & Tang, Haiyan, 2022. "Numerical weather prediction enhanced wind power forecasting: Rank ensemble and probabilistic fluctuation awareness," Applied Energy, Elsevier, vol. 313(C).
    21. Yang, Yuqing & Bremner, Stephen & Menictas, Chris & Kay, Merlinde, 2022. "Forecasting error processing techniques and frequency domain decomposition for forecasting error compensation and renewable energy firming in hybrid systems," Applied Energy, Elsevier, vol. 313(C).
    22. Elizabeth Michael, Neethu & Hasan, Shazia & Al-Durra, Ahmed & Mishra, Manohar, 2022. "Short-term solar irradiance forecasting based on a novel Bayesian optimized deep Long Short-Term Memory neural network," Applied Energy, Elsevier, vol. 324(C).
    23. Zhang, Jiaan & Liu, Dong & Li, Zhijun & Han, Xu & Liu, Hui & Dong, Cun & Wang, Junyan & Liu, Chenyu & Xia, Yunpeng, 2021. "Power prediction of a wind farm cluster based on spatiotemporal correlations," Applied Energy, Elsevier, vol. 302(C).
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