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An integrated framework of Bi-directional long-short term memory (BiLSTM) based on sine cosine algorithm for hourly solar radiation forecasting

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  • Peng, Tian
  • Zhang, Chu
  • Zhou, Jianzhong
  • Nazir, Muhammad Shahzad

Abstract

Accurate and reliable solar radiation forecasting is of great significance for the management and utilization of solar energy. This study proposes a deep learning model based on Bi-directional long short-term memory (BiLSTM), sine cosine algorithm (SCA) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) for solar radiation forecasting. Firstly, the CEEMDAN is applied to decompose the stochastic historical time series into certain periodic intrinsic mode functions (IMFs) and a residual. Secondly, the significant antecedent solar radiation patterns of the decomposed sub-modes are identified via two statistical techniques, namely, the autocorrelation function (ACF) and the partial autocorrelation function (PACF). Thirdly, all the sub-modes are forecasted using the BiLSTM model, and the parameters of the BiLSTM model are optimized using the SCA algorithm. Finally, the forecasted sub-modes are aggregated to generate the final forecasting result. The accuracy of the proposed deep learning model is investigated by applying it in forecasting hourly solar radiation of four real-world datasets over multi-step horizons. Comparative experiments with other seven models demonstrate the effectiveness of the integrated model, the CEEMDAN technique and the SCA algorithm, respectively. The proposed model can obtain higher prediction accuracy than the existing models for all datasets and forecasting horizons.

Suggested Citation

  • Peng, Tian & Zhang, Chu & Zhou, Jianzhong & Nazir, Muhammad Shahzad, 2021. "An integrated framework of Bi-directional long-short term memory (BiLSTM) based on sine cosine algorithm for hourly solar radiation forecasting," Energy, Elsevier, vol. 221(C).
    • Handle: RePEc:eee:energy:v:221:y:2021:i:c:s0360544221001365
    DOI: 10.1016/j.energy.2021.119887
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    References listed on IDEAS

    as
    1. Liu, Hui & Chen, Chao, 2019. "Multi-objective data-ensemble wind speed forecasting model with stacked sparse autoencoder and adaptive decomposition-based error correction," Applied Energy, Elsevier, vol. 254(C).
    2. Li, Danny H.W. & Chen, Wenqiang & Li, Shuyang & Lou, Siwei, 2019. "Estimation of hourly global solar radiation using Multivariate Adaptive Regression Spline (MARS) – A case study of Hong Kong," Energy, Elsevier, vol. 186(C).
    3. Chu Zhang & Tian Peng & Chaoshun Li & Wenlong Fu & Xin Xia & Xiaoming Xue, 2019. "Multiobjective Optimization of a Fractional-Order PID Controller for Pumped Turbine Governing System Using an Improved NSGA-III Algorithm under Multiworking Conditions," Complexity, Hindawi, vol. 2019, pages 1-18, February.
    4. Jahani, Babak & Dinpashoh, Y. & Raisi Nafchi, Atefeh, 2017. "Evaluation and development of empirical models for estimating daily solar radiation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 878-891.
    5. Ghimire, Sujan & Deo, Ravinesh C. & Raj, Nawin & Mi, Jianchun, 2019. "Wavelet-based 3-phase hybrid SVR model trained with satellite-derived predictors, particle swarm optimization and maximum overlap discrete wavelet transform for solar radiation prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    6. Pedro, Hugo T.C. & Coimbra, Carlos F.M. & David, Mathieu & Lauret, Philippe, 2018. "Assessment of machine learning techniques for deterministic and probabilistic intra-hour solar forecasts," Renewable Energy, Elsevier, vol. 123(C), pages 191-203.
    7. Fan, Junliang & Chen, Baiquan & Wu, Lifeng & Zhang, Fucang & Lu, Xianghui & Xiang, Youzhen, 2018. "Evaluation and development of temperature-based empirical models for estimating daily global solar radiation in humid regions," Energy, Elsevier, vol. 144(C), pages 903-914.
    8. Chen, Ji-Long & He, Lei & Yang, Hong & Ma, Maohua & Chen, Qiao & Wu, Sheng-Jun & Xiao, Zuo-lin, 2019. "Empirical models for estimating monthly global solar radiation: A most comprehensive review and comparative case study in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 91-111.
    9. Fan, Junliang & Wu, Lifeng & Ma, Xin & Zhou, Hanmi & Zhang, Fucang, 2020. "Hybrid support vector machines with heuristic algorithms for prediction of daily diffuse solar radiation in air-polluted regions," Renewable Energy, Elsevier, vol. 145(C), pages 2034-2045.
    10. Jiang, He & Dong, Yao, 2017. "Global horizontal radiation forecast using forward regression on a quadratic kernel support vector machine: Case study of the Tibet Autonomous Region in China," Energy, Elsevier, vol. 133(C), pages 270-283.
    11. Kulshrestha, Anurag & Krishnaswamy, Venkataraghavan & Sharma, Mayank, 2020. "Bayesian BILSTM approach for tourism demand forecasting," Annals of Tourism Research, Elsevier, vol. 83(C).
    12. Mohammad Rezaie-Balf & Niloofar Maleki & Sungwon Kim & Ali Ashrafian & Fatemeh Babaie-Miri & Nam Won Kim & Il-Moon Chung & Sina Alaghmand, 2019. "Forecasting Daily Solar Radiation Using CEEMDAN Decomposition-Based MARS Model Trained by Crow Search Algorithm," Energies, MDPI, vol. 12(8), pages 1-23, April.
    13. Ghimire, Sujan & Deo, Ravinesh C. & Raj, Nawin & Mi, Jianchun, 2019. "Deep solar radiation forecasting with convolutional neural network and long short-term memory network algorithms," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    14. Li, Chaoshun & Tang, Geng & Xue, Xiaoming & Chen, Xinbiao & Wang, Ruoheng & Zhang, Chu, 2020. "The short-term interval prediction of wind power using the deep learning model with gradient descend optimization," Renewable Energy, Elsevier, vol. 155(C), pages 197-211.
    15. Peng, Tian & Zhang, Chu & Zhou, Jianzhong & Nazir, Muhammad Shahzad, 2020. "Negative correlation learning-based RELM ensemble model integrated with OVMD for multi-step ahead wind speed forecasting," Renewable Energy, Elsevier, vol. 156(C), pages 804-819.
    16. Dong, Jin & Olama, Mohammed M. & Kuruganti, Teja & Melin, Alexander M. & Djouadi, Seddik M. & Zhang, Yichen & Xue, Yaosuo, 2020. "Novel stochastic methods to predict short-term solar radiation and photovoltaic power," Renewable Energy, Elsevier, vol. 145(C), pages 333-346.
    17. Wang, Yamin & Wu, Lei, 2016. "On practical challenges of decomposition-based hybrid forecasting algorithms for wind speed and solar irradiation," Energy, Elsevier, vol. 112(C), pages 208-220.
    18. Long, Huan & Zhang, Zijun & Su, Yan, 2014. "Analysis of daily solar power prediction with data-driven approaches," Applied Energy, Elsevier, vol. 126(C), pages 29-37.
    19. Monjoly, Stéphanie & André, Maïna & Calif, Rudy & Soubdhan, Ted, 2017. "Hourly forecasting of global solar radiation based on multiscale decomposition methods: A hybrid approach," Energy, Elsevier, vol. 119(C), pages 288-298.
    20. Gairaa, Kacem & Khellaf, Abdallah & Messlem, Youcef & Chellali, Farouk, 2016. "Estimation of the daily global solar radiation based on Box–Jenkins and ANN models: A combined approach," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 238-249.
    21. Qing, Xiangyun & Niu, Yugang, 2018. "Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM," Energy, Elsevier, vol. 148(C), pages 461-468.
    22. Prasad, Ramendra & Ali, Mumtaz & Kwan, Paul & Khan, Huma, 2019. "Designing a multi-stage multivariate empirical mode decomposition coupled with ant colony optimization and random forest model to forecast monthly solar radiation," Applied Energy, Elsevier, vol. 236(C), pages 778-792.
    23. Feng, Yu & Hao, Weiping & Li, Haoru & Cui, Ningbo & Gong, Daozhi & Gao, Lili, 2020. "Machine learning models to quantify and map daily global solar radiation and photovoltaic power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 118(C).
    24. Liu, Yongqi & Qin, Hui & Zhang, Zhendong & Pei, Shaoqian & Wang, Chao & Yu, Xiang & Jiang, Zhiqiang & Zhou, Jianzhong, 2019. "Ensemble spatiotemporal forecasting of solar irradiation using variational Bayesian convolutional gate recurrent unit network," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    25. Voyant, Cyril & Muselli, Marc & Paoli, Christophe & Nivet, Marie-Laure, 2012. "Numerical weather prediction (NWP) and hybrid ARMA/ANN model to predict global radiation," Energy, Elsevier, vol. 39(1), pages 341-355.
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