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Forecasting and Uncertainty Analysis of Day-Ahead Photovoltaic Power Based on WT-CNN-BiLSTM-AM-GMM

Author

Listed:
  • Bo Gu

    (School of Electrical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450011, China)

  • Xi Li

    (School of Electrical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450011, China)

  • Fengliang Xu

    (Xinyang Power Supply Company of State Grid Henan Electric Power Company, Xinyang 464000, China)

  • Xiaopeng Yang

    (Henan Jiuyu Tenglong Information Engineering Co., Ltd., Zhengzhou 450052, China)

  • Fayi Wang

    (Xinyang Power Supply Company of State Grid Henan Electric Power Company, Xinyang 464000, China)

  • Pengzhan Wang

    (Henan Jiuyu Tenglong Information Engineering Co., Ltd., Zhengzhou 450052, China)

Abstract

Accurate forecasting of photovoltaic (PV) power is of great significance for the safe, stable, and economical operation of power grids. Therefore, a day-ahead photovoltaic power forecasting (PPF) and uncertainty analysis method based on WT-CNN-BiLSTM-AM-GMM is proposed in this paper. Wavelet transform (WT) is used to decompose numerical weather prediction (NWP) data and photovoltaic power data into frequency data with time information, which eliminates the influence of randomness and volatility in the data information on the forecasting accuracy. A convolutional neural network (CNN) is used to deeply mine the seasonal characteristics of the input data and the correlation characteristics between the input data. The bidirectional long short-term memory network (BiLSTM) is used to deeply explore the temporal correlation of the input data series. To reflect the different influences of the input data sequence on the model forecasting accuracy, the weight of the calculated value of the BiLSTM model for each input data is adaptively adjusted using the attention mechanism (AM) algorithm according to the data sequence, which further improves the model forecasting accuracy. To accurately calculate the probability density distribution characteristics of photovoltaic forecasting errors, the Gaussian mixture model (GMM) method was used to calculate the probability density distribution of forecasting errors, and the confidence interval of the day-ahead PPF was calculated. Using a photovoltaic power station as the calculation object, the forecasting results of the WT-CNN-BiLSTM-AM, CNN-BiLSTM, WT-CNN-BiLSTM, long short-term memory network (LSTM), gate recurrent unit (GRU), and PSO-BP models were compared and analyzed. The calculation results show that the forecasting accuracy of the WT-CNN-BiLSTM-AM model is higher than that of the other models. The confidence interval coverage calculated from the GMM is greater than the given confidence level.

Suggested Citation

  • Bo Gu & Xi Li & Fengliang Xu & Xiaopeng Yang & Fayi Wang & Pengzhan Wang, 2023. "Forecasting and Uncertainty Analysis of Day-Ahead Photovoltaic Power Based on WT-CNN-BiLSTM-AM-GMM," Sustainability, MDPI, vol. 15(8), pages 1-27, April.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:8:p:6538-:d:1121748
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    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. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
    3. Aslam, Sheraz & Herodotou, Herodotos & Mohsin, Syed Muhammad & Javaid, Nadeem & Ashraf, Nouman & Aslam, Shahzad, 2021. "A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    4. Qu, Jiaqi & Qian, Zheng & Pei, Yan, 2021. "Day-ahead hourly photovoltaic power forecasting using attention-based CNN-LSTM neural network embedded with multiple relevant and target variables prediction pattern," Energy, Elsevier, vol. 232(C).
    5. Khan, Waqas & Walker, Shalika & Zeiler, Wim, 2022. "Improved solar photovoltaic energy generation forecast using deep learning-based ensemble stacking approach," Energy, Elsevier, vol. 240(C).
    6. Han, Shuang & Qiao, Yan-hui & Yan, Jie & Liu, Yong-qian & Li, Li & Wang, Zheng, 2019. "Mid-to-long term wind and photovoltaic power generation prediction based on copula function and long short term memory network," Applied Energy, Elsevier, vol. 239(C), pages 181-191.
    7. Huang, Xiaoqiao & Li, Qiong & Tai, Yonghang & Chen, Zaiqing & Zhang, Jun & Shi, Junsheng & Gao, Bixuan & Liu, Wuming, 2021. "Hybrid deep neural model for hourly solar irradiance forecasting," Renewable Energy, Elsevier, vol. 171(C), pages 1041-1060.
    8. Wang, Jianzhou & Zhou, Yilin & Li, Zhiwu, 2022. "Hour-ahead photovoltaic generation forecasting method based on machine learning and multi objective optimization algorithm," Applied Energy, Elsevier, vol. 312(C).
    9. Liu, Luyao & Zhao, Yi & Chang, Dongliang & Xie, Jiyang & Ma, Zhanyu & Sun, Qie & Yin, Hongyi & Wennersten, Ronald, 2018. "Prediction of short-term PV power output and uncertainty analysis," Applied Energy, Elsevier, vol. 228(C), pages 700-711.
    10. Gu, Bo & Shen, Huiqiang & Lei, Xiaohui & Hu, Hao & Liu, Xinyu, 2021. "Forecasting and uncertainty analysis of day-ahead photovoltaic power using a novel forecasting method," Applied Energy, Elsevier, vol. 299(C).
    11. 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.
    12. Sam Sugiyama, 2007. "Forecast Uncertainty and Monte Carlo Simulation," Foresight: The International Journal of Applied Forecasting, International Institute of Forecasters, issue 6, pages 29-37, Spring.
    13. Mayer, Martin János & Gróf, Gyula, 2021. "Extensive comparison of physical models for photovoltaic power forecasting," Applied Energy, Elsevier, vol. 283(C).
    14. van der Meer, D.W. & Widén, J. & Munkhammar, J., 2018. "Review on probabilistic forecasting of photovoltaic power production and electricity consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1484-1512.
    15. Mellit, A. & Pavan, A. Massi & Lughi, V., 2021. "Deep learning neural networks for short-term photovoltaic power forecasting," Renewable Energy, Elsevier, vol. 172(C), pages 276-288.
    16. Schinke-Nendza, A. & von Loeper, F. & Osinski, P. & Schaumann, P. & Schmidt, V. & Weber, C., 2021. "Probabilistic forecasting of photovoltaic power supply — A hybrid approach using D-vine copulas to model spatial dependencies," Applied Energy, Elsevier, vol. 304(C).
    Full references (including those not matched with items on IDEAS)

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    2. Xin Yan & Qian Zhang, 2023. "Research on Combination of Distributed Generation Placement and Dynamic Distribution Network Reconfiguration Based on MIBWOA," Sustainability, MDPI, vol. 15(12), pages 1-34, June.
    3. Gu, Bo & Zhang, Hongtao & Yue, Shuai & Suslov, Konstantin & Shi, Jie, 2025. "Fault warning study of gearbox based on SOM-ASTGCN-BiLSTM and mutual diagnosis of same clustered wind turbines," Renewable Energy, Elsevier, vol. 251(C).
    4. Mahtab Murshed & Manohar Chamana & Konrad Erich Kork Schmitt & Suhas Pol & Olatunji Adeyanju & Stephen Bayne, 2023. "Sizing PV and BESS for Grid-Connected Microgrid Resilience: A Data-Driven Hybrid Optimization Approach," Energies, MDPI, vol. 16(21), pages 1-22, October.

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