DOI: https://doi.org/10.14710/ijred.2022.44755

Daily Solar Radiation Forecasting based on a Hybrid NARX-GRU Network in Dumaguete, Philippines

School of Chemical, Biological, and Materials Engineering and Sciences, Mapúa University, Manila 1002, Philippines

Received: 14 Feb 2022; Revised: 28 May 2022; Accepted: 6 Jun 2022; Available online: 18 Jun 2022; Published: 4 Aug 2022.
Editor(s): H. Hadiyanto
Open Access Copyright (c) 2022 The Author(s). 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

In recent years, solar radiation forecasting has become highly important worldwide as solar energy increases its contribution to electricity grids. However, due to the intermittent nature of solar radiation caused by meteorological parameters, forecasting errors arise, and fluctuations in the power output of photovoltaic (PV) systems become a severe issue. This paper aims to introduce a forecasting hybrid model of daily global solar radiation time series. Meteorological data and solar radiation samples from Dumaguete, Philippines, are used to assess the forecasting accuracy of the proposed nonlinear autoregressive network with exogenous inputs (NARX) – gated recurrent unit (GRU) hybrid model. Four different models were trained using the meteorological and solar radiation data, which are the Optimizable Gaussian Process Regression (GPR), Nonlinear Autoregressive Network (NAR), NARX, and the proposed Hybrid NARX-GRU Network.  Results show that the hybrid NARX-GRU model has a root mean square error (RMSE) of ~0.05 and a training time of 33 seconds. The proposed hybrid model has better forecasting performance compared to the three models which obtained RMSE values of 27.741, 39.82, and 28.92, for the GPR, NAR, and NARX, respectively. The simulation results demonstrate that the NARX-GRU model significantly outperforms the regression and single models in terms of statistical metrics and training efficiency. Furthermore, this study shows that the hybridized NARX-GRU model is able to provide an effective estimation for daily global solar radiation, which is important in the operation of PV plants in the country, specifically for unit commitment purposes

Fulltext View|Download
Keywords: Forecasting; NARX-GRU; Neural network; Photovoltaic system; Solar Radiation

Article Metrics:

Article Info
Section: Original Research Article
Language : EN
Recent articles
The Effect of Trailing Edge Profile Modifications to Fluid-Structure Interactions of a Vertical Axis Tidal Turbine Blade Investigation of the Impact of Large-Scale Wind Power and Solar Power Plants on a Vietnamese Transmission Network Energy Management Strategy Based on Marine Predators Algorithm for Grid-Connected Microgrid More recent articles
Most cited articles
Design, Analysis and Optimization of a Solar Dish/Stirling System Generating Organic Liquid Products from Catalytic Cracking of Used Cooking Oil over Mechanically Mixed Catalysts A novel single input double output (SIDO) converter for torque ripple minimization in solar powered BLDC motor Biodiesel Production From the Microalgae Nannochloropsis by Microwave Using CaO and MgO Catalysts Influence of the Rubber Seed Type and Altitude on Characteristic of Seed, Oil and Biodiesel More cited articles
  1. Abdel-Basset, M., Hawash, H., Chakrabortty, R. K., & Ryan, M. (2021). PV-Net: An innovative deep learning approach for efficient forecasting of short-term photovoltaic energy production. Journal of Cleaner Production, 303, 127037. https://doi.org/10.1016/J.JCLEPRO.2021.127037
  2. Ahmad, A., Anderson, T. N., & Lie, T. T. (2015). Hourly global solar irradiation forecasting for New Zealand. Solar Energy, 122, 1398–1408. https://doi.org/10.1016/J.SOLENER.2015.10.055
  3. Akhter, M. N., Mekhilef, S., Mokhlis, H., Ali, R., Usama, M., Muhammad, M. A., & Khairuddin, A. S. M. (2021). A hybrid deep learning method for an hour ahead power output forecasting of three different photovoltaic systems. Applied Energy, 118185. https://doi.org/10.1016/J.APENERGY.2021.118185
  4. Al-Ghezi, M. K., Mahmoud, B. K., Alnasser, T. M., & Chaichan, M. T. (2022). A Comparative Study of Regression Models and Meteorological Parameters to Estimate the Global Solar Radiation on a Horizontal Surface for Baghdad City, Iraq. International Journal of Renewable Energy Development, 11(1), 71-81. https://doi.org/10.14710/ijred.2022.38493
  5. Almonacid, F., Pérez-Higueras, P. J., Fernández, E. F., & Hontoria, L. (2014). A methodology based on dynamic artificial neural network for short-term forecasting of the power output of a PV generator. Energy Conversion and Management, 85, 389–398. https://doi.org/10.1016/j.enconman.2014.05.090
  6. Antonanzas, J., Osorio, N., Escobar, R., Urraca, R., Martinez-de-Pison, F. J., & Antonanzas-Torres, F. (2016). Review of photovoltaic power forecasting. Solar Energy, 136, 78–111. https://doi.org/10.1016/J.SOLENER.2016.06.069
  7. ArunKumar, K. E., Kalaga, D. v., Kumar, C. M. S., Kawaji, M., & Brenza, T. M. (2021). Forecasting of COVID-19 using deep layer Recurrent Neural Networks (RNNs) with Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTM) cells. Chaos, Solitons & Fractals, 146, 110861. https://doi.org/10.1016/J.CHAOS.2021.110861
  8. Awad, M., & Khanna, R. (2015). Machine Learning. In Efficient Learning Machines (pp. 1–18). Apress. https://doi.org/10.1007/978-1-4302-5990-9_1
  9. Belmahdi, B., Louzazni, M., & Bouardi, A. el. (2020). One month-ahead forecasting of mean daily global solar radiation using time series models. Optik, 219, 165207. https://doi.org/10.1016/J.IJLEO.2020.165207
  10. Chakrabarty, A., Danielson, C., Bortoff, S. A., & Laughman, C. R. (2021). Accelerating self-optimization control of refrigerant cycles with Bayesian optimization and adaptive moment estimation. Applied Thermal Engineering, 197, 117335. https://doi.org/10.1016/J.APPLTHERMALENG.2021.117335
  11. Cho, K., van Merriënboer, B., Gulcehre, C., Bahdanau, D., Bougares, F., Schwenk, H., & Bengio, Y. (2014). Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation. Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), 1724–1734. https://doi.org/10.3115/v1/D14-1179
  12. Deutsche GIZ GmbH, Renewable Energy Developers Center, & WWF Philippines. (2013). IT’S MORE SUN IN THE PHILIPPINES Facts and Figures on Solar Energy in the Philippines Project Development Programme (PDP) Southeast-Asia. https://www.doe.gov.ph/sites/default/files/pdf/netmeter/policy-brief-its-more-sun-in-the-philippines-V3.pdf
  13. Diagne, H. M., David, M., Lauret, P., Boland, J., Schmutz, N., Review, N. S., & Ma¨ımouna Diagne, M. (n.d.). Review of solar irradiance forecasting methods and a proposition for small-scale insular grids. Renewable and Sustainable Energy Reviews, 27, 65–76. https://doi.org/10.1016/j.rser.2013.06.042
  14. Duffie, J. A., Beckman, W. A., and Worek, W. M. (1994). Solar Engineering of Thermal Processes, 2nd ed.. ASME. J. Sol. Energy Eng. February 1994;. https://doi.org/10.1115/1.2930068
  15. Faisal, A. N. M. F., Rahman, A., Habib, M. T. M., Siddique, A. H., Hasan, M., & ; Khan, M. M. (2022). Neural networks based multivariate time series forecasting of solar radiation using meteorological data of different cities of Bangladesh. Results in Engineering, 13, 100365. https://doi.org/10.1016/J.RINENG.2022.100365
  16. Gonzaga Baca Ruiz, L., Pegalajar Cuéllar, M., Delgado Calvo-Flores, M., del Carmen Pegalajar Jiménez, M., Riquelme, J. C., Troncoso, A., & Martínez-Álvarez, F. (2016). An Application of Non-Linear Autoregressive Neural Networks to Predict Energy Consumption in Public Buildings. Energies, 9(9). https://doi.org/10.3390/en9090684
  17. Huang, X., Li, Q., Tai, Y., Chen, Z., Zhang, J., Shi, J., Gao, B., & Liu, W. (2021). Hybrid deep neural model for hourly solar irradiance forecasting. Renewable Energy, 171, 1041–1060. https://doi.org/10.1016/J.RENENE.2021.02.161
  18. Husein, M., & Chung, I.-Y. (2019). Day-Ahead Solar Irradiance Forecasting for Microgrids Using a Long Short-Term Memory Recurrent Neural Network: A Deep Learning Approach. Energies, 12(10). https://doi.org/10.3390/en12101856
  19. Jaihuni, M., Basak, J. K., Khan, F., Okyere, F. G., Sihalath, T., Bhujel, A., Park, J., Lee, D. H., & Kim, H. T. (2021). A novel recurrent neural network approach in forecasting short term solar irradiance. ISA Transactions
  20. https://doi.org/10.1016/j.isatra.2021.03.043
  21. Jung, Y., Jung, J., Kim, B., & Han, S. U. (2020). Long short-term memory recurrent neural network for modeling temporal patterns in long-term power forecasting for solar PV facilities: Case study of South Korea. Journal of Cleaner Production, 250, 119476. https://doi.org/10.1016/J.JCLEPRO.2019.119476
  22. Khan, A. T., Khan, A. R., Li, S., Bakhsh, S., Mehmood, A., & Zaib, J. (2021). Optimally configured Gated Recurrent Unit using Hyperband for the long-term forecasting of photovoltaic plant. Renewable Energy Focus, 39, 49–58. https://doi.org/10.1016/J.REF.2021.07.002
  23. Kumari, P., & Toshniwal, D. (2021). Deep learning models for solar irradiance forecasting: A comprehensive review. Journal of Cleaner Production, 318, 128566. https://doi.org/10.1016/J.JCLEPRO.2021.128566
  24. Lai, C. S., Zhong, C., Pan, K., Ng, W. W. Y., & Lai, L. L. (2021). A deep learning based hybrid method for hourly solar radiation forecasting. Expert Systems with Applications, 177, 114941. https://doi.org/10.1016/j.eswa.2021.114941
  25. Li, J., Ward, J. K., Tong, J., Collins, L., & Platt, G. (2016). Machine learning for solar irradiance forecasting of photovoltaic system. Renewable Energy, 90, 542–553. https://doi.org/10.1016/j.renene.2015.12.069
  26. Li, P., Zhou, K., Lu, X., & Yang, S. (2020). A hybrid deep learning model for short-term PV power forecasting. Applied Energy, 259, 114216. https://doi.org/10.1016/J.APENERGY.2019.114216
  27. Liu, X., Lin, Z., & Feng, Z. (2021). Short-term offshore wind speed forecast by seasonal ARIMA - A comparison against GRU and LSTM. Energy, 227, 120492. https://doi.org/10.1016/J.ENERGY.2021.120492
  28. Liu, Y., Qin, H., Zhang, Z., Pei, S., Wang, C., Yu, X., Jiang, Z., & Zhou, J. (2019). Ensemble spatiotemporal forecasting of solar irradiation using variational Bayesian convolutional gate recurrent unit network. Applied Energy, 253, 113596. https://doi.org/10.1016/J.APENERGY.2019.113596
  29. Mellit, A., Pavan, A. M., & Lughi, V. (2021). Deep learning neural networks for short-term photovoltaic power forecasting. Renewable Energy, 172, 276–288. https://doi.org/10.1016/J.RENENE.2021.02.166
  30. Najibi, F., Apostolopoulou, D., & Alonso, E. (2021). Enhanced performance Gaussian process regression for probabilistic short-term solar output forecast. International Journal of Electrical Power & Energy Systems, 130, 106916. https://doi.org/10.1016/J.IJEPES.2021.106916
  31. Narvaez, G., Giraldo, L. F., Bressan, M., & Pantoja, A. (2021). Machine learning for site-adaptation and solar radiation forecasting. Renewable Energy, 167, 333–342. https://doi.org/10.1016/j.renene.2020.11.089
  32. Pazikadin, A. R., Rifai, D., Ali, K., Malik, M. Z., Abdalla, A. N., & Faraj, M. A. (2020). Solar irradiance measurement instrumentation and power solar generation forecasting based on Artificial Neural Networks (ANN): A review of five years research trend. Science of the Total Environment, 715, 136848. https://doi.org/10.1016/j.scitotenv.2020.136848
  33. Pisoni, E., Farina, M., Carnevale, C., & Piroddi, L. (2009). Forecasting peak air pollution levels using NARX models. Engineering Applications of Artificial Intelligence, 22(4), 593–602. https://doi.org/10.1016/j.engappai.2009.04.002
  34. Puah, B. K., Chong, L. W., Wong, Y. W., Begam, K. M., Khan, N., Juman, M. A., & Rajkumar, R. K. (2021). A regression unsupervised incremental learning algorithm for solar irradiance prediction. Renewable Energy, 164, 908–925. https://doi.org/10.1016/j.renene.2020.09.080
  35. Rohani, A., Taki, M., & Abdollahpour, M. (2018). A novel soft computing model (Gaussian process regression with K-fold cross validation) for daily and monthly solar radiation forecasting (Part: I). Renewable Energy, 115, 411–422. https://doi.org/10.1016/J.RENENE.2017.08.061
  36. Sharma, A., & Kakkar, A. (2018). Forecasting daily global solar irradiance generation using machine learning. In Renewable and Sustainable Energy Reviews (Vol. 82, pp. 2254–2269). Elsevier Ltd. https://doi.org/10.1016/j.rser.2017.08.066
  37. Wang, F., Xuan, Z., Zhen, Z., Li, K., Wang, T., & Shi, M. (2020). A day-ahead PV power forecasting method based on LSTM-RNN model and time correlation modification under partial daily pattern prediction framework. Energy Conversion and Management, 212, 112766. https://doi.org/10.1016/J.ENCONMAN.2020.112766
  38. Wang, H., Cai, R., Zhou, B., Aziz, S., Qin, B., Voropai, N., Gan, L., & Barakhtenko, E. (2020). Solar irradiance forecasting based on direct explainable neural network. Energy Conversion and Management, 226, 113487. https://doi.org/10.1016/j.enconman.2020.113487
  39. Yadav, A. K., & Chandel, S. S. (2014). Solar radiation prediction using Artificial Neural Network techniques: A review. Renewable and Sustainable Energy Reviews, 33, 772–781. https://doi.org/10.1016/J.RSER.2013.08.055

Last update:

  1. Forecasting Directions, Dates, And Causes of Future Technological Revolutions concerning the Growth of Human Capital

    Ning Chen, Masoud Vaseei, Ali Peivandizadeh, Peiman Ghasemi. Discrete Dynamics in Nature and Society, 2022 (1), 2022. doi: 10.1155/2022/2494916

  2. Application of Artificial Intelligence Model Solar Radiation Prediction for Renewable Energy Systems

    Hasan Alkahtani, Theyazn H. H. Aldhyani, Saleh Nagi Alsubari. Sustainability, 15 (8), 2023. doi: 10.3390/su15086973

  3. A hybrid network of NARX and DS-attention applied for the state estimation of lithium-ion batteries

    Zhehao Xia, Yizhong Wu. International Journal of Electrochemical Science, 19 (7), 2024. doi: 10.1016/j.ijoes.2024.100632

  4. Optimized Random Forest for Solar Radiation Prediction Using Sunshine Hours

    Cesar Villegas-Mier, Juvenal Rodriguez-Resendiz, José Álvarez-Alvarado, Hugo Jiménez-Hernández, Ákos Odry. Micromachines, 13 (9), 2022. doi: 10.3390/mi13091406

  5. Review on the Application of Photovoltaic Forecasting Using Machine Learning for Very Short- to Long-Term Forecasting

    Putri Nor Liyana Mohamad Radzi, Muhammad Naveed Akhter, Saad Mekhilef, Noraisyah Mohamed Shah. Sustainability, 15 (4), 2023. doi: 10.3390/su15042942

Last update: 2024-11-14 17:08:53

No citation recorded.