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Research on short-term photovoltaic power prediction based on multi-scale similar days and ESN-KELM dual core prediction model

Author

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  • Li, Naiqing
  • Li, Longhao
  • Zhang, Fan
  • Jiao, Ticao
  • Wang, Shuang
  • Liu, Xuefeng
  • Wu, Xinghua

Abstract

Photovoltaic (PV) power generation has emerged as an essential means of developing and utilizing new energy. Accurate PV power prediction is critical for building a new power system generation and guaranteeing system stability when a high proportion of renewable energy is connected. Therefore, this research proposes a hybrid prediction method based on multi-scale similar days and ESN-KELM dual-kernel prediction to increase the prediction accuracy of PV power generation. First, the multi-scale similar days algorithm is used to determine similar days of the forecast day as the model training data. This operation can reduce the impact of the randomness of PV power output on the model performance. Second, the hidden features of PV power are mined using a fast iterative filter decomposition method. Based on the complexity of the components, the corresponding ESN-KELM dual-kernel prediction models are established. An improved Archimedes optimization approach is used to optimize the ESN-KELM model's parameters. Next, the predicted power is obtained by aggregating the predicted results of each component. Ultimately, the method is validated using historical operational data from PV power plants. The results indicate that the proposed model can achieve well prediction results for various seasons and weather conditions.

Suggested Citation

  • Li, Naiqing & Li, Longhao & Zhang, Fan & Jiao, Ticao & Wang, Shuang & Liu, Xuefeng & Wu, Xinghua, 2023. "Research on short-term photovoltaic power prediction based on multi-scale similar days and ESN-KELM dual core prediction model," Energy, Elsevier, vol. 277(C).
    • Handle: RePEc:eee:energy:v:277:y:2023:i:c:s0360544223009519
    DOI: 10.1016/j.energy.2023.127557
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    1. Das, Utpal Kumar & Tey, Kok Soon & Seyedmahmoudian, Mehdi & Mekhilef, Saad & Idris, Moh Yamani Idna & Van Deventer, Willem & Horan, Bend & Stojcevski, Alex, 2018. "Forecasting of photovoltaic power generation and model optimization: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 912-928.
    2. Heo, Jae & Song, Kwonsik & Han, SangUk & Lee, Dong-Eun, 2021. "Multi-channel convolutional neural network for integration of meteorological and geographical features in solar power forecasting," Applied Energy, Elsevier, vol. 295(C).
    3. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda, 2019. "A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    4. Sarah Feron & Raúl R. Cordero & Alessandro Damiani & Robert B. Jackson, 2021. "Climate change extremes and photovoltaic power output," Nature Sustainability, Nature, vol. 4(3), pages 270-276, March.
    5. Bonanno, F. & Capizzi, G. & Graditi, G. & Napoli, C. & Tina, G.M., 2012. "A radial basis function neural network based approach for the electrical characteristics estimation of a photovoltaic module," Applied Energy, Elsevier, vol. 97(C), pages 956-961.
    6. Cabaneros, Sheen Mclean & Calautit, John Kaiser & Hughes, Ben, 2020. "Spatial estimation of outdoor NO2 levels in Central London using deep neural networks and a wavelet decomposition technique," Ecological Modelling, Elsevier, vol. 424(C).
    7. Alessandrini, S. & Delle Monache, L. & Sperati, S. & Cervone, G., 2015. "An analog ensemble for short-term probabilistic solar power forecast," Applied Energy, Elsevier, vol. 157(C), pages 95-110.
    8. Korkmaz, Deniz, 2021. "SolarNet: A hybrid reliable model based on convolutional neural network and variational mode decomposition for hourly photovoltaic power forecasting," Applied Energy, Elsevier, vol. 300(C).
    9. Hassan, Muhammed A. & Bailek, Nadjem & Bouchouicha, Kada & Nwokolo, Samuel Chukwujindu, 2021. "Ultra-short-term exogenous forecasting of photovoltaic power production using genetically optimized non-linear auto-regressive recurrent neural networks," Renewable Energy, Elsevier, vol. 171(C), pages 191-209.
    10. Kushwaha, Vishal & Pindoriya, Naran M., 2019. "A SARIMA-RVFL hybrid model assisted by wavelet decomposition for very short-term solar PV power generation forecast," Renewable Energy, Elsevier, vol. 140(C), pages 124-139.
    11. Donghun Lee & Kwanho Kim, 2019. "Recurrent Neural Network-Based Hourly Prediction of Photovoltaic Power Output Using Meteorological Information," Energies, MDPI, vol. 12(2), pages 1-22, January.
    12. Sun, Xianke & Wang, Gaoliang & Xu, Liuyang & Yuan, Honglei & Yousefi, Nasser, 2021. "Optimal estimation of the PEM fuel cells applying deep belief network optimized by improved archimedes optimization algorithm," Energy, Elsevier, vol. 237(C).
    Full references (including those not matched with items on IDEAS)

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