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A novel method based on time series ensemble model for hourly photovoltaic power prediction

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  • Xiao, Zenan
  • Huang, Xiaoqiao
  • Liu, Jun
  • Li, Chengli
  • Tai, Yonghang

Abstract

Photovoltaic (PV) power generation technology is more and more widely used in smart grids. Accurate prediction of PV power is very important for managing and planning of the power grid system. However, solar energy has great randomness, intermittency, and uncertainty, which makes it difficult for the existing photovoltaic power prediction methods to achieve satisfactory prediction accuracy. To improve the comprehensive prediction performance of the model, this work proposes a reliable photovoltaic power prediction model based on neural-prophet (NP), convolutional neural network (CNN), and long short-term memory (LSTM) models. In this paper, multiple components of the NP are used to model the PV data and finish the preliminary prediction, then multiple features from NP combined with meteorological data are sent to the CNN-LSTM model, then used CNN-LSTM to extract the internal characteristics of the trend and seasonal variables of the PV data to achieves more accurate forecasting results. Finally, there are several evaluation indicators such as root mean square error (RMSE) and mean absolute error (MAE) to verify the performance of the proposed model. The results show that compared with the traditional LSTM model, CNN model, and the single NP model, the model proposed in this paper has a better effect on PV power prediction. The RMSE and MAE of the proposed model reached 0.987 kW and 0.563 kW, respectively.

Suggested Citation

  • Xiao, Zenan & Huang, Xiaoqiao & Liu, Jun & Li, Chengli & Tai, Yonghang, 2023. "A novel method based on time series ensemble model for hourly photovoltaic power prediction," Energy, Elsevier, vol. 276(C).
    • Handle: RePEc:eee:energy:v:276:y:2023:i:c:s0360544223009362
    DOI: 10.1016/j.energy.2023.127542
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    References listed on IDEAS

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    5. Zang, Haixiang & Chen, Dianhao & Liu, Jingxuan & Cheng, Lilin & Sun, Guoqiang & Wei, Zhinong, 2024. "Improving ultra-short-term photovoltaic power forecasting using a novel sky-image-based framework considering spatial-temporal feature interaction," Energy, Elsevier, vol. 293(C).
    6. Mirza, Adeel Feroz & Mansoor, Majad & Usman, Muhammad & Ling, Qiang, 2023. "A comprehensive approach for PV wind forecasting by using a hyperparameter tuned GCVCNN-MRNN deep learning model," Energy, Elsevier, vol. 283(C).
    7. Yuhan Wu & Chun Xiang & Heng Qian & Peijian Zhou, 2024. "Optimization of Bi-LSTM Photovoltaic Power Prediction Based on Improved Snow Ablation Optimization Algorithm," Energies, MDPI, vol. 17(17), pages 1-21, September.
    8. Mirza, Adeel Feroz & Shu, Zhaokun & Usman, Muhammad & Mansoor, Majad & Ling, Qiang, 2024. "Quantile-transformed multi-attention residual framework (QT-MARF) for medium-term PV and wind power prediction," Renewable Energy, Elsevier, vol. 220(C).
    9. Yin, Linfei & Zheng, Da, 2024. "Decomposition prediction fractional-order PID reinforcement learning for short-term smart generation control of integrated energy systems," Applied Energy, Elsevier, vol. 355(C).
    10. Hu, Zehuan & Gao, Yuan & Ji, Siyu & Mae, Masayuki & Imaizumi, Taiji, 2024. "Improved multistep ahead photovoltaic power prediction model based on LSTM and self-attention with weather forecast data," Applied Energy, Elsevier, vol. 359(C).

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