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Ensemble Interval Prediction for Solar Photovoltaic Power Generation

Author

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  • Yaxin Zhang

    (School of Mathematical Sciences, Capital Normal University, Beijing 100048, China)

  • Tao Hu

    (School of Mathematical Sciences, Capital Normal University, Beijing 100048, China)

Abstract

In recent years, solar photovoltaic power generation has emerged as an essential means of energy supply. The prediction of its active power is not only conducive to cost saving but can also promote the development of solar power generation industry. However, it is challenging to obtain an accurate and high-quality interval prediction of active power. Based on the data set of desert knowledge in the Australia solar center in Australia, firstly, we have compared twelve interval prediction methods based on machine learning. Secondly, six ensemble methods, namely Ensemble-Mean, Ensemble-Median (Ensemble-Med), Ensemble-Envelop (Ensemble-En), Ensemble-Probability averaging of endpoints and simple averaging of midpoints (Ensemble-PM), Ensemble-Exterior trimming (Ensemble-TE), and Ensemble-Interior trimming (Ensemble-TI) are used to combine forecast intervals. The result indicates that Ensemble-TE is the best method. Additionally, compared to other methods, Ensemble-TE ensures the prediction interval coverage probability for confidence levels of 95%, 90%, 85%, and 80% as 0.960, 0.920, 0.873, and 0.824, respectively, using 15-min level data. Meanwhile, the narrower prediction interval normalized averaged width is calculated for the same confidence levels as 0.066, 0.045, 0.035, and 0.028, respectively. In addition, higher Winkler score and smaller coverage width-based criterion are obtained, representing high-quality intervals. We have calculated smaller mean prediction interval center deviation, which is approximately 0.044. Thus, the above demonstrates that this study obtains the prediction interval with better performance compared to other existing methods.

Suggested Citation

  • Yaxin Zhang & Tao Hu, 2022. "Ensemble Interval Prediction for Solar Photovoltaic Power Generation," Energies, MDPI, vol. 15(19), pages 1-30, September.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:19:p:7193-:d:929641
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    References listed on IDEAS

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    1. 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).
    2. Wen-He Chen & Long-Sheng Cheng & Zhi-Peng Chang & Han-Ting Zhou & Qi-Feng Yao & Zhai-Ming Peng & Li-Qun Fu & Zong-Xiang Chen & Giacomo Fiumara, 2022. "Interval Prediction of Photovoltaic Power Using Improved NARX Network and Density Peak Clustering Based on Kernel Mahalanobis Distance," Complexity, Hindawi, vol. 2022, pages 1-22, March.
    3. Yusen Wang & Wenlong Liao & Yuqing Chang, 2018. "Gated Recurrent Unit Network-Based Short-Term Photovoltaic Forecasting," Energies, MDPI, vol. 11(8), pages 1-14, August.
    4. Huang, Jing & Perry, Matthew, 2016. "A semi-empirical approach using gradient boosting and k-nearest neighbors regression for GEFCom2014 probabilistic solar power forecasting," International Journal of Forecasting, Elsevier, vol. 32(3), pages 1081-1086.
    5. Liu, Xiaolei & Lin, Zi & Feng, Ziming, 2021. "Short-term offshore wind speed forecast by seasonal ARIMA - A comparison against GRU and LSTM," Energy, Elsevier, vol. 227(C).
    6. Benali, L. & Notton, G. & Fouilloy, A. & Voyant, C. & Dizene, R., 2019. "Solar radiation forecasting using artificial neural network and random forest methods: Application to normal beam, horizontal diffuse and global components," Renewable Energy, Elsevier, vol. 132(C), pages 871-884.
    7. Anil Gaba & Ilia Tsetlin & Robert L. Winkler, 2017. "Combining Interval Forecasts," Decision Analysis, INFORMS, vol. 14(1), pages 1-20, March.
    8. Hosenuzzaman, M. & Rahim, N.A. & Selvaraj, J. & Hasanuzzaman, M. & Malek, A.B.M.A. & Nahar, A., 2015. "Global prospects, progress, policies, and environmental impact of solar photovoltaic power generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 41(C), pages 284-297.
    9. Jing Lei & Max G’Sell & Alessandro Rinaldo & Ryan J. Tibshirani & Larry Wasserman, 2018. "Distribution-Free Predictive Inference for Regression," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 113(523), pages 1094-1111, July.
    10. Ying Wang & Bo Feng & Qing-Song Hua & Li Sun, 2021. "Short-Term Solar Power Forecasting: A Combined Long Short-Term Memory and Gaussian Process Regression Method," Sustainability, MDPI, vol. 13(7), pages 1-16, March.
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    1. Wen-Chang Tsai & Chia-Sheng Tu & Chih-Ming Hong & Whei-Min Lin, 2023. "A Review of State-of-the-Art and Short-Term Forecasting Models for Solar PV Power Generation," Energies, MDPI, vol. 16(14), pages 1-30, July.

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