DOI: https://doi.org/10.14710/ijred.9.2.207-216

An Efficient Algorithm for Power Prediction in PV Generation System

Department of Electrical Power and Mechatronics, Tafila Technical University, Jordan

Received: 18 Jan 2020; Revised: 5 Mar 2020; Accepted: 15 Apr 2020; Available online: 3 May 2020; Published: 15 Jul 2020.
Editor(s): H Hadiyanto
Open Access Copyright (c) 2020 The Authors. Published by Centre of Biomass and Renewable Energy (CBIORE)
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Citation Format:
Abstract

Aiming at the existing photovoltaic power generation prediction methods, the modeling is complicated, the prediction accuracy is low, and it is difficult to meet the actual needs. Based on the improvement of the traditional wavelet neural network, a dual-mode cuckoo search wavelet neural network algorithm combined prediction method is proposed, which takes into account the extraction of chaotic features of surface solar radiation and photovoltaic output power. The proposed algorithm first reconstructs the chaotic phase space of the hidden information of each influencing factor in the data history of PV generation and according to the correlation analysis, the solar radiation is utilized as additional input. Next, the proposed algorithm overcomes the limitations of the cuckoo search algorithm such as the sensitivity to the initial value and searchability and convergence speed by dual-mode cuckoo search wavelet neural network algorithm. Lastly, a prediction model of the proposed algorithm is proposed and the prediction analysis is performed under different weather conditions. Simulation results show that the proposed algorithm shows better performance than the existing algorithms under different weather conditions. Under various weather conditions, the mean values of TIC, EMAE and ENRMSE error indicators of the proposed forecasting algorithm were reduced by 43.70%, 45.75%, and 45.41%, respectively. Compared with the Chaos-WNN prediction method, the prediction performance has been further improved under various weather conditions and the mean values of TIC, EMAE and ENRMSE error indicators have been reduced by 25.55%, 27.26%, and 36.83%, respectively. 

Fulltext View|Download
Keywords: Renewable energy; PV; optimization; chaotic feature extraction; DMCS-WNN

Article Metrics:

Article Info
Section: Original Research Article
Language : EN
Recent articles
Numerical Analysis of Energy Converter for Wave Energy Power Generation-Pendulum System Bioethanol Production from Sugarcane Bagasse Using Neurospora intermedia in an Airlift Bioreactor Investigation of Electrochemical, Thermal and Electrical Performance of 3D Lithium-Ion Battery Module in a High -Temperature Environment Distributed Generation: A Critical Review of Technologies, Grid Integration Issues, Growth Drivers and Potential Benefits More recent articles
Most cited articles
Multi-Criteria Decision Making (MCDM) Approach for Selecting Solar Plants Site and Technology: A Review Castor Seed from Melkasa Agricultural Research Centre, East Showa, Ethiopia and it’s biodiesel performance in Four Stroke Diesel Engine Melting Behavior of Phase Change Material in a Solar Vertical Thermal Energy Storage with Variable Length Fins added on the Heat Transfer Tube Surfaces Modeling and Experimental Validation of a Transient Direct Expansion Heat Pump Study of Fabricated Solar Dryer of Tomato Slices under Jordan Climate Condition More cited articles
  1. Akhter, M. N., Mekhilef, S., Mokhlis, H., Mohamed Shah, N. (2019) Review on Forecasting of Photovoltaic Power Generation Based on Machine Learning and Metaheuristic Techniques, IET Renewable Power Generation, 13(7), 1009-1023. https://doi.org/10.1049/iet-rpg.2018.5649
  2. Brano, V. L., Ciulla, G., Falco, M. D. (2014). Artificial Neural Networks to Predict the Power Output of a PV Panel," International Journal of Photoenergy, 193083, 1-12. https://doi.org/10.1155/2014/193083
  3. Chen, J., Xin, B., Peng, Z., Dou, L., Zhang, J. (2009). Optimal Contraction Theorem for Exploration-Exploitation Tradeoff in Search and Optimization. IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans, 39(3), 680-691,. https://doi.org/10.1109/TSMCA.2009.2012436
  4. Das, U. K., Tey, K. S., Seyedmahmoudian, M., Mekhilef, S., Idris, M.Y.I., Deventer, W.V., Horan, B., Stojcevski, A. (2018). Forecasting of Photovoltaic Power Generation and Model Optimization: A Review. Renewable and Sustainable Energy Reviews, 81(1), 912-928. https://doi.org/10.1016/j.rser.2017.08.017
  5. Du P., Zhang, G., Li P., Li, M., Liu, H., Hou, J. (2019). The Photovoltaic Output Prediction Based on Variational Mode Decomposition and Maximum Relevance Minimum Redundancy. Applied Sciences, 9(17),1-16. https://doi.org/10.3390/app9173593
  6. Huang, C. J. and Kuo, P. H. (2019). Multiple-Input Deep Convolutional Neural Network Model for Short-Term Photovoltaic Power Forecasting. IEEE Access, 7, 74822-74834. https://doi.org/10.1109/ACCESS.2019.2921238
  7. Kang, M., Sohn, J., Park, J., Lee, S. and Yoon, Y. (2011). Development of Algorithm for Day Ahead PV Generation Forecasting Using Data Mining Method. IEEE 54th International Midwest Symposium on Circuits and Systems (MWSCAS), Seoul, South Korea, pp. 1-4. https://doi.org/10.1109/MWSCAS.2011.6026333
  8. Khan, I., Zhu, H., Yao, J., Khan, D. and Iqbal, T. (2017). Hybrid Power Forecasting Model for Photovoltaic Plants Based on Neural Network with Air Quality Index. International Journal of Photoenergy, 6938713, 1-9. https://doi.org/10.1155/2017/6938713
  9. Kiml, H. S., Eykholt, R., Sals, J. D. (1999). Nonlinear Dynammics, Delay Time, and Embedding Windows. Physical D, 12(1), 48-60. https://doi.org/10.1016/S0167-2789(98)00240-1
  10. Li, J., You, H., Qi, J., Kong, M., Zhang, S. and Zhang, H. (2019). Stratified Optimization Strategy Used for Restoration with Photovoltaic-Battery Energy Storage Systems as Black-Start Resources. IEEE Access, 7, 117339-127352, 2019. https://doi.org/10.1109/ACCESS.2019.2937833
  11. Li, L.-L., Cheng, P., Lin, H.-C., & Dong, H. (2017). Short-term output power forecasting of Photovoltaic Systems Based on Deep Belied Net. Advances in Mechanical Engineering, 9(9), 1-13. https://doi.org/10.1177/1687814017715983
  12. Liu, J., Fang, W., Zhang, X., Yang, C. (2015). An Improved Photovoltaic Power Forecasting Model with the Assistance of Aerosol Index Data. IEEE Transactions on Sustainable Energy, 62(2). 434-442. https://doi.org/10.1109/TSTE.2014.2381224
  13. Liu, Y., Shi, J., Yang, Y., Lee, W. (2012) Short-Term Wind-Power Prediction Based on Wavelet Transform-support Vector Machine and Statistic-Characteristics Analysis, IEEE Transactions on Industry Applications, 48(4), 1136-1141. https://doi.org/10.1109/TIA.2012.2199449
  14. Mei F, Wu Q, Shi T, Lu J, Pan Y, Zheng J, (2019). An Ultrashort-Term Net Load Forecasting Model Based on Phase Space Reconstruction and Deep Neural Network. Applied Sciences, 9(7), 1-11. https://doi.org/10.3390/app9071487
  15. Munawar, U and Wang, Z. (2020). A Framework of Using Machine Learning Approaches for Short-Term Solar Power Forecasting. Journal of Electrical Engineering & Technology, 1-9. https://doi.org/10.1007/s42835-020-00346-4
  16. Nespoli A, Ogliari E, Leva S, Massi Pavan A, Mellit A, Lughi V, Dolara A. (2019). Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques. Energies, 12(9), 1-14. https://doi.org/10.3390/en12091621
  17. Qiu, J., An, X.J., Wu, Z.G. and Li, F.F., (2020). Forecasting solar irradiation based on influencing factors determined by linear correlation and stepwise regression. Theoretical and Applied Climatology, 140, 253-269. https://doi.org/10.1007/s00704-019-03072-8
  18. Rosato, A., Altilio, R., Araneo, R. and Panella, M. (2017). Prediction in Photovoltaic Power by Neural Networks. Energies, 10, 1-25. https://doi.org/10.3390/en10071003
  19. Saberian, A., Hizam, H., Radzi, M. A. M., Kadir M. Z. A. Ab. and Mirzaei M. (2014). Modeling and Prediction of Photovoltaic Power Output Using Artificial Neural Networks. International Journal of Photoenergy, 469701,1-10. https://doi.org/10.1155/2014/469701
  20. Sheng, H., Xiao, J. , Cheng, Y., Ni, Q., and Wang, S. (2018). Short term solar power forecasting based on weighted Gaussian Process Regression, IEEE Transactions on Industrial Electronics, 65(1), 300-308, 2018. https://doi.org/10.1109/TIE.2017.2714127
  21. Sun Y, Wang F, Wang B, Chen Q, Engerer N, Mi Z. (2016). Correlation Feature Selection and Mutual Information Theory Based Quantitative Research on Meteorological Impact Factors of Module Temperature for Solar Photovoltaic Systems. Energies, 10(1), 1-20. https://doi.org/10.3390/en10010007
  22. Teng, Y., Hui, Q., Li, Y., Leng, O. and Chen, Z. (2019) Availability Estimation of Wind Power Forecasting and Optimization of Day-Ahead Unit Commitment. Journal of Modern Power Systems and Clean Energy, 7, 1675-1683. https://doi.org/10.1007/s40565-019-00571-5
  23. The Official Website of Queenland University, Queenland, Australia: University of Queensland Solar Photovoltaic Data. 2017. http://www.solar.uq.edu.au/
  24. Wang, Q., Ji, S., Hu, M., Li, W., Liu, F., and Zhu, L. (2018). Short-Term Photovoltaic Power Generation Combination Forecasting Method Based on Similar Day and Cross Entropy Theory. International Journal of Photoenergy, 6973297, 1-10. https://doi.org/10.1155/2018/6973297
  25. Xie T, Zhang G, Liu H, Liu F, Du P. (2018). A Hybrid Forecasting Method for Solar Output Power Based on Variational Mode Decomposition, Deep Belief Networks, and Auto-Regressive Moving Average. Applied Sciences, 8, 1-24. https://doi.org/10.3390/app8101901
  26. Yang, X. S., Deb, S., Cuckoo Search via Lévy Flights (2009). Proceedings of the IEEE World Congress on Nature & Biologically Inspired Computing, Coimbatore, India, 210-214. https://doi.org/10.1109/NABIC.2009.5393690
  27. Zhong, Z., Yang, C., Cao, W., Yan, C. (2017). Short-Term Photovoltaic Power Generation Forecasting Based on Multivariable Grey Theory Model with Parameter Optimization. Mathematical Problems in Engineering, 5812394, 1-9. https://doi.org/10.1155/2017/5812394

Last update:

  1. An Optimal Approach for PV Power Optimization Based on Neural Network

    Qais Alsafasfeh, William Brown, Omar A. Saraereh. 2023 IEEE/IAS 59th Industrial and Commercial Power Systems Technical Conference (I&CPS), 2023. doi: 10.1109/ICPS57144.2023.10142129

  2. A high accurate user-friendly energy audit platform of a university building using ANN Bayesian regularization and Levenberg-Marquardt algorithm

    Ferdinand L. Marcos, Boonyang Plangklang. Energy Reports, 11 , 2024. doi: 10.1016/j.egyr.2024.01.062

  3. Harnessing artificial intelligence for data-driven energy predictive analytics: A systematic survey towards enhancing sustainability

    Thanh Tuan Le, Jayabal Chandra Priya, Huu Cuong Le, Nguyen Viet Linh Le, Minh Thai Duong, Dao Nam Cao. International Journal of Renewable Energy Development, 13 (2), 2024. doi: 10.61435/ijred.2024.60119

Last update: 2024-04-24 06:29:38

No citation recorded.