IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v11y2019i15p4138-d253401.html
   My bibliography  Save this article

A Hybrid Model Based on Principal Component Analysis, Wavelet Transform, and Extreme Learning Machine Optimized by Bat Algorithm for Daily Solar Radiation Forecasting

Author

Listed:
  • Xing Zhang

    (Department of Business Administration, North China Electric Power University, Baoding 071000, China)

  • Zhuoqun Wei

    (Department of Business Administration, North China Electric Power University, Baoding 071000, China)

Abstract

Precise solar radiation forecasting is of great importance for solar energy utilization and its integration into the grid, but because of the daily solar radiation’s intrinsic non-stationary and nonlinearity, which is influenced by a lot of elements, single predicting models may have difficulty obtaining results with high accuracy. Therefore, this paper innovatively puts forward an original hybrid model that predicts solar radiation through extreme learning machine (ELM) optimized by the bat algorithm (BA) based on wavelet transform (WT) and principal component analysis (PCA). First, choose the meteorological variables on the basis of Pearson coefficient test, and WT will decompose historical solar radiation into two time series, which are de-noised signal and noise signal. In the approximate series, the lag phase of historical radiation is obtained by partial autocorrelation function (PACF). After that, use PCA to reduce the dimensions of the influencing factors, including meteorological variables and historical radiation. Finally, ELM is established to predict daily solar radiation, whose input weight and deviation thresholds gained optimization by BA, thus it is called BA-ELM henceforth. In view of the four distinct solar radiation series obtained by NASA, the empirical simulation explained the hybrid model’s validity and effectiveness compared to other primary methods.

Suggested Citation

  • Xing Zhang & Zhuoqun Wei, 2019. "A Hybrid Model Based on Principal Component Analysis, Wavelet Transform, and Extreme Learning Machine Optimized by Bat Algorithm for Daily Solar Radiation Forecasting," Sustainability, MDPI, vol. 11(15), pages 1-20, July.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:15:p:4138-:d:253401
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/11/15/4138/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/11/15/4138/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Mellit, A. & Benghanem, M. & Kalogirou, S.A., 2006. "An adaptive wavelet-network model for forecasting daily total solar-radiation," Applied Energy, Elsevier, vol. 83(7), pages 705-722, July.
    2. Amrouche, Badia & Le Pivert, Xavier, 2014. "Artificial neural network based daily local forecasting for global solar radiation," Applied Energy, Elsevier, vol. 130(C), pages 333-341.
    3. Chen, S.X. & Gooi, H.B. & Wang, M.Q., 2013. "Solar radiation forecast based on fuzzy logic and neural networks," Renewable Energy, Elsevier, vol. 60(C), pages 195-201.
    4. Voyant, Cyril & Paoli, Christophe & Muselli, Marc & Nivet, Marie-Laure, 2013. "Multi-horizon solar radiation forecasting for Mediterranean locations using time series models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 44-52.
    5. Meenal, R. & Selvakumar, A. Immanuel, 2018. "Assessment of SVM, empirical and ANN based solar radiation prediction models with most influencing input parameters," Renewable Energy, Elsevier, vol. 121(C), pages 324-343.
    6. Trapero, Juan R. & Kourentzes, Nikolaos & Martin, A., 2015. "Short-term solar irradiation forecasting based on Dynamic Harmonic Regression," Energy, Elsevier, vol. 84(C), pages 289-295.
    7. Audi, M.S. & Alsaad, M.A., 1991. "Simple hourly global solar radiation prediction models," Renewable Energy, Elsevier, vol. 1(3), pages 473-478.
    8. 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.
    9. Tan, Zhongfu & Zhang, Jinliang & Wang, Jianhui & Xu, Jun, 2010. "Day-ahead electricity price forecasting using wavelet transform combined with ARIMA and GARCH models," Applied Energy, Elsevier, vol. 87(11), pages 3606-3610, November.
    10. Li, Song & Goel, Lalit & Wang, Peng, 2016. "An ensemble approach for short-term load forecasting by extreme learning machine," Applied Energy, Elsevier, vol. 170(C), pages 22-29.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Yinghui Meng & Sultan Noman Qasem & Manouchehr Shokri & Shahab S, 2020. "Dimension Reduction of Machine Learning-Based Forecasting Models Employing Principal Component Analysis," Mathematics, MDPI, vol. 8(8), pages 1-15, July.
    2. L. M. Fernández-Ahumada & J. Ramírez-Faz & R. López-Luque & A. Márquez-García & M. Varo-Martínez, 2019. "A Methodology for Buildings Access to Solar Radiation in Sustainable Cities," Sustainability, MDPI, vol. 11(23), pages 1-17, November.
    3. Na Sun & Nan Zhang & Shuai Zhang & Tian Peng & Wei Jiang & Jie Ji & Xiangmiao Hao, 2022. "An Integrated Framework Based on an Improved Gaussian Process Regression and Decomposition Technique for Hourly Solar Radiation Forecasting," Sustainability, MDPI, vol. 14(22), pages 1-22, November.
    4. Meysam Alizamir & Kaywan Othman Ahmed & Jalal Shiri & Ahmad Fakheri Fard & Sungwon Kim & Salim Heddam & Ozgur Kisi, 2023. "A New Insight for Daily Solar Radiation Prediction by Meteorological Data Using an Advanced Artificial Intelligence Algorithm: Deep Extreme Learning Machine Integrated with Variational Mode Decomposit," Sustainability, MDPI, vol. 15(14), pages 1-35, July.
    5. Muhammad Aslam & Jae-Myeong Lee & Mustafa Raed Altaha & Seung-Jae Lee & Sugwon Hong, 2020. "AE-LSTM Based Deep Learning Model for Degradation Rate Influenced Energy Estimation of a PV System," Energies, MDPI, vol. 13(17), pages 1-14, August.
    6. Wei Sun & Junjian Zhang, 2020. "Carbon Price Prediction Based on Ensemble Empirical Mode Decomposition and Extreme Learning Machine Optimized by Improved Bat Algorithm Considering Energy Price Factors," Energies, MDPI, vol. 13(13), pages 1-22, July.
    7. Yi Liang & Haichao Wang & Wei-Chiang Hong, 2021. "Sustainable Development Evaluation of Innovation and Entrepreneurship Education of Clean Energy Major in Colleges and Universities Based on SPA-VFS and GRNN Optimized by Chaos Bat Algorithm," Sustainability, MDPI, vol. 13(11), pages 1-26, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Si-Ya Wang & Jun Qiu & Fang-Fang Li, 2018. "Hybrid Decomposition-Reconfiguration Models for Long-Term Solar Radiation Prediction Only Using Historical Radiation Records," Energies, MDPI, vol. 11(6), pages 1-17, May.
    2. Ping-Huan Kuo & Chiou-Jye Huang, 2018. "A Green Energy Application in Energy Management Systems by an Artificial Intelligence-Based Solar Radiation Forecasting Model," Energies, MDPI, vol. 11(4), pages 1-15, April.
    3. Heng, Jiani & Wang, Jianzhou & Xiao, Liye & Lu, Haiyan, 2017. "Research and application of a combined model based on frequent pattern growth algorithm and multi-objective optimization for solar radiation forecasting," Applied Energy, Elsevier, vol. 208(C), pages 845-866.
    4. 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.
    5. Lima, Marcello Anderson F.B. & Carvalho, Paulo C.M. & Fernández-Ramírez, Luis M. & Braga, Arthur P.S., 2020. "Improving solar forecasting using Deep Learning and Portfolio Theory integration," Energy, Elsevier, vol. 195(C).
    6. Kaur, Amanpreet & Nonnenmacher, Lukas & Coimbra, Carlos F.M., 2016. "Net load forecasting for high renewable energy penetration grids," Energy, Elsevier, vol. 114(C), pages 1073-1084.
    7. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
    8. Pin Li & Jinsuo Zhang, 2019. "Is China’s Energy Supply Sustainable? New Research Model Based on the Exponential Smoothing and GM(1,1) Methods," Energies, MDPI, vol. 12(2), pages 1-30, January.
    9. Marzouq, Manal & El Fadili, Hakim & Zenkouar, Khalid & Lakhliai, Zakia & Amouzg, Mohammed, 2020. "Short term solar irradiance forecasting via a novel evolutionary multi-model framework and performance assessment for sites with no solar irradiance data," Renewable Energy, Elsevier, vol. 157(C), pages 214-231.
    10. Voyant, Cyril & Soubdhan, Ted & Lauret, Philippe & David, Mathieu & Muselli, Marc, 2015. "Statistical parameters as a means to a priori assess the accuracy of solar forecasting models," Energy, Elsevier, vol. 90(P1), pages 671-679.
    11. Sun, Wei & Zhang, Chongchong, 2018. "Analysis and forecasting of the carbon price using multi—resolution singular value decomposition and extreme learning machine optimized by adaptive whale optimization algorithm," Applied Energy, Elsevier, vol. 231(C), pages 1354-1371.
    12. Sepasi, Saeed & Reihani, Ehsan & Howlader, Abdul M. & Roose, Leon R. & Matsuura, Marc M., 2017. "Very short term load forecasting of a distribution system with high PV penetration," Renewable Energy, Elsevier, vol. 106(C), pages 142-148.
    13. Wang, Yuhan & Zhang, Chu & Fu, Yongyan & Suo, Leiming & Song, Shihao & Peng, Tian & Shahzad Nazir, Muhammad, 2023. "Hybrid solar radiation forecasting model with temporal convolutional network using data decomposition and improved artificial ecosystem-based optimization algorithm," Energy, Elsevier, vol. 280(C).
    14. Elham Alzain & Shaha Al-Otaibi & Theyazn H. H. Aldhyani & Ali Saleh Alshebami & Mohammed Amin Almaiah & Mukti E. Jadhav, 2023. "Revolutionizing Solar Power Production with Artificial Intelligence: A Sustainable Predictive Model," Sustainability, MDPI, vol. 15(10), pages 1-21, May.
    15. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
    16. Federico Divina & Aude Gilson & Francisco Goméz-Vela & Miguel García Torres & José F. Torres, 2018. "Stacking Ensemble Learning for Short-Term Electricity Consumption Forecasting," Energies, MDPI, vol. 11(4), pages 1-31, April.
    17. Ahmad, Tanveer & Zhang, Dongdong & Huang, Chao, 2021. "Methodological framework for short-and medium-term energy, solar and wind power forecasting with stochastic-based machine learning approach to monetary and energy policy applications," Energy, Elsevier, vol. 231(C).
    18. Rodrigues, Eugénio & Gomes, Álvaro & Gaspar, Adélio Rodrigues & Henggeler Antunes, Carlos, 2018. "Estimation of renewable energy and built environment-related variables using neural networks – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 959-988.
    19. 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.
    20. Sen Wang & Yonghui Sun & Yan Zhou & Rabea Jamil Mahfoud & Dongchen Hou, 2019. "A New Hybrid Short-Term Interval Forecasting of PV Output Power Based on EEMD-SE-RVM," Energies, MDPI, vol. 13(1), pages 1-17, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:11:y:2019:i:15:p:4138-:d:253401. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.