Journal of Applied Science and Engineering

Published by Tamkang University Press

ESCI jase impact factor scopus logo open access rate of Scopus journal

Short-term photovoltaic power prediction based on FVS-KELM method

 2020

 Volume 23, Issue 2

Jun Li1,2,3 and Meng Li1

1School of Automation and Electrical Engineering, Lanzhou Jiaotong University, Lanzhou, Gansu 730070, P.R. China

2Gansu Provincial Engineering Technology Center for Informatization of Logistics Transport Equipment, Lanzhou 730070, P.R.China

3Gansu Provincial Industry Technology Center of Logistics Transport Equipment. Lanzhou 730070, P.R. China

Received: August 29, 2019
Accepted: February 17, 2020
Publication Date: May 10, 2026

上傳圖片

Architecture of FVS-KELM method

 Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation:  BibTeX | http://dx.doi.org/10.6180/jase.202006_23(2).0013  

Download PDF

For the highly nonlinear short-term photovoltaic power problems affected by a variety of factors, a novel modeling method FVS-KELM based on feature vectors selection (FVS) algorithm and kernel extreme learning machine (KELM) is proposed. Firstly, FVS algorithm maps the input data to the high-dimensional feature space through kernel trick, considering the geometric features, selecting the relevant data subset to form the base vector of the subspace. Secondly, the input data is projected onto the feature subspace, and the final prediction model is established by the KELM method, which does not need set the number of the hidden layer nodes, and uses the kernel function representing the unknown nonlinear feature mapping of the hidden layer, so it has good generalization ability. In order to verify the effectiveness of the FVS-KELM modeling method, it is applied to the benchmark photovoltaic power prediction example provided by Global Energy Forecasting Competition 2014 (GEFCOM2014). Under the same condition, the FVS-KELM method is compared with extreme learning machine (ELM), support vector machine (SVM) method etc. The experimental results show that the FVS-KELM method can not only reduce the computational complexity, but also has good prediction accuracy and robustness, and the model has good generalization.

Keywords: Photovoltaic Power; Prediction; Feature Vectors Selection; Extreme Learning Machine; Kernel Method

  1. [1] Adil Ahmed and Muhammad Khalid. A review on the selected applications of forecasting models in renewable power systems. Renewable and Sustainable Energy Reviews, 100:9–21, 2019.
  2. [2] Sobrina Sobri, Sam Koohi-Kamali, and Nasrudin Abd Rahim. Solar photovoltaic generation forecasting methods: A review. Energy Conversion and Management, 156:459–497, 2018.
  3. [3] Florian Barbieri, Sumedha Rajakaruna, and Arindam Ghosh. Very short-term photovoltaic power forecasting with cloud modeling: A review. Renewable and Sustainable Energy Reviews, 75:242–263, 2017.
  4. [4] Aidan Tuohy, John Zack, Sue Ellen Haupt, Justin Sharp, Mark Ahlstrom, Skip Dise, Eric Grimit, Corinna Mohrlen, Matthias Lange, and Mayte Garcia Casado. Solar forecasting: methods, challenges, and performance. IEEE Power and Energy Magazine, 13(6):50–59, 2015.
  5. [5] Alberto Dolara, Sonia Leva, and Giampaolo Manzolini. Comparison of different physical models for PV power output prediction. Solar energy, 119:83–99, 2015.
  6. [6] Hugo H. T. C. Pedro and Carlos F. M. Coimbra. Assessment of forecasting techniques for solar power production with no exogenous inputs. Solar Energy, 86(7):20172028, 2012.
  7. [7] Yanting Li, Yan Su, and Lianjie Shu. An ARMAX model for forecasting the power output of a grid connected photovoltaic system. Renewable Energy, 66:78–89, 2014.
  8. [8] Remco A. Verzijlbergh, Petra W. Heijnen, Stephan R. de Roode, Alexander Los, and Harm J. J. Jonker. Improved model output statistics of numerical weather prediction based irradiance forecasts for solar power applications. Solar Energy, 118:634–645, 2015.
  9. [9] Cyril Voyant, Gilles Notton, Soteris Kalogirou, Marie-Laure Nivet, Christophe Paoli, Fabrice Motte, and Alexis Fouilloy. Machine learning methods for solar radiation forecasting: A review. Renewable Energy, 105:569–582, 2017.
  10. [10] Guido Cervone, Laura Clemente-Harding, Stefano Alessandrini, and Luca Delle Monache. Short-term photovoltaic power forecasting using Artificial Neural Networks and an Analog Ensemble. Renewable Energy, 108:274–286, 2017.
  11. [11] Xianshuang Yao, Zhanshan Wang, and Huaguang Zhang. A novel photovoltaic power forecasting model based on echo state network. Neurocomputing, 325:182189, 2019.
  12. [12] Sonia Leva, Alberto Dolara, Francesco Grimaccia, Marco Mussetta, and Emanuele Ogliari. Analysis and validation of 24 hours ahead neural network forecasting of photovoltaic output power. Mathematics and computers in simulation, 131:88–100, 2017.
  13. [13] William VanDeventer, Elmira Jamei, Gokul Sidarth Thirunavukkarasu, Mehdi Seyedmahmoudian, Tey Kok Soon, Ben Horan, Saad Mekhilef, and Alex Stojcevski. Short-term PV power forecasting using hybrid GASVM technique. Renewable energy, 140:367–379, 2019.
  14. [14] Moufida Bouzerdoum, Adel Mellit, and A. Massi Pavan. A hybrid model (SARIMA–SVM) for short-term power forecasting of a small-scale grid-connected photovoltaic plant. Solar Energy, 98:226–235, 2013.
  15. [15] Abinet Tesfaye Eseye, Jianhua Zhang, and Dehua Zheng. Short-term photovoltaic solar power forecasting using a hybrid Wavelet-PSO-SVM model based on SCADA and Meteorological information. Renewable Energy, 118:357–367, 2018.
  16. [16] Maria Malvoni, Maria Grazia De Giorgi, and Paolo Maria Congedo. Photovoltaic forecast based on hybrid PCA–LSSVM using dimensionality reducted data. Neurocomputing, 211:72–83, 2016.
  17. [17] Guang-Bin Huang, Qin-Yu Zhu, and Chee-Kheong Siew. Extreme learning machine: theory and applications. Neurocomputing, 70(1-3):489–501, 2006.
  18. [18] Sameer Al-Dahidi, Osama Ayadi, Jehad Adeeb, Mohammad Alrbai, and Bashar R. Qawasmeh. Extreme learning machines for solar photovoltaic power predictions. Energies, 11(10):2725, 2018.
  19. [19] Shahaboddin Shamshirband, Kasra Mohammadi, Lip Yee, Dalibor Petković, and Ali Mostafaeipour. A comparative evaluation for identifying the suitability of extreme learning machine to predict horizontal global solar radiation. Renewable and sustainable energy reviews, 52:1031–1042, 2015.
  20. [20] Pingzhou Tang, Di Chen, and Yushuo Hou. Entropy method combined with extreme learning machine method for the short-term photovoltaic power generation forecasting. Chaos, Solitons & Fractals, 89:243248, 2016.
  21. [21] Jun Li and Da-Chao Li. Wind power time series prediction using optimized kernel extreme learning machine method. Acta Physica Sinica, 65(13):130501, 2016.
  22. [22] Federica Davò, Stefano Alessandrini, Simone Sperati, Luca Delle Monache, Davide Airoldi, and Maria T. Vespucci. Post-processing techniques and principal component analysis for regional wind power and solar irradiance forecasting. Solar Energy, 134:327–338, 2016.
  23. [23] Chunxia Dou, Hang Qi, Wei Luo, and Yamin Zhang. Elman neural network based short-term photovoltaic power forecasting using association rules and kernel principal component analysis. Journal of Renewable and Sustainable Energy, 10(4):43501, 2018.
  24. [24] Gaston Baudat and Fatiha Anouar. Feature vector selection and projection using kernels. Neurocomputing, 55(1-2):21–38, 2003.
  25. [25] Yao Zhang and Jianxue Wang. GEFCom2014 probabilistic solar power forecasting based on k-nearest neighbor and kernel density estimator. In 2015 IEEE Power & Energy Society General Meeting, pages 1–5. IEEE, 2015.
  26. [26] Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli, and Rob J. Hyndman. Probabilistic energy forecasting: Global energy forecasting competition 2014 and beyond. International Journal of Forecasting, 32(3):896–913, 2016.
  27. [27] Azhar Ahmed Mohammed and Zeyar Aung. Ensemble learning approach for probabilistic forecasting of solar power generation. Energies, 9(12):1017, 2016.