Prediction of photovoltaic power using the Johansen VECM cointegration method in the Reduit region, Mauritius

References

• Ahmed, A., and M. Khalid. 2019. A review on the selected applications of forecasting models in renewable power systems. Renewable and Sustainable Energy Reviews 100:9–21. doi:10.1016/j.rser.2018.09.046.
   Web of Science ®Google Scholar
• AlSkaif, T., S. Dev, L. Visser, M. Hossari, and W. van Sark. 2020. A systematic analysis of meteorological variables for PV output power estimation. Renewable Energy 153:12–22. doi:10.1016/j.renene.2020.01.150.
   Web of Science ®Google Scholar
• Andrei, D., and L. Andrei. 2015. Vector error correction model in explaining the association of some macroeconomic variables in Romania. Procedia Economics and Finance 22:568–76. doi:10.1016/S2212-5671(15)00261-0.
   Google Scholar
• Antonanzas, J., N. Osorio, R. Escobar, R. Urraca, F. J. Martinez-de-Pison, and F. Antonanzas-Torres. 2016. Review of photovoltaic power forecasting. Solar Energy 136:78–111. doi:10.1016/j.solener.2016.06.069.
   Web of Science ®Google Scholar
• Bacher, P., H. Madsen, and H. Nielsen. 2009. Online short-term solar power forecasting. Solar Energy 83 (10):1772–83. doi:10.1016/j.solener.2009.05.016.
   Web of Science ®Google Scholar
• Bessa, R., A. Trindade, C. Silva, and V. Miranda. 2015. Probabilistic solar power forecasting in smart grids using distributed information. International Journal of Electrical Power & Energy Systems 72:16–23. doi:10.1016/j.ijepes.2015.02.006.
   Web of Science ®Google Scholar
• Beyer, G. G., J. P. Martinez, M. Suri, J. L. Torres, E. Lorenz, S. Muller, C. Hoyer-Klick, and P. Ineichen. 2009. Benchmarking of radiation products, Mesor Report D.1.1.3.
   Google Scholar
• Bouzerdoum, M., A. Mellit, and A. Massi Pavan. 2013. A hybrid model (SARIMA-SVM) for short-term power forecasting of a small-scale grid-connected photovoltaic plant. Solar Energy 98:226–35. doi:10.1016/j.solener.2013.10.002.
   Web of Science ®Google Scholar
• Casin, P. 2009. Econométrie méthodes et applications avec Eviews: Editor Technip. Available from https://www.eyrolles.com/Entreprise/Livre/econometrie-9782710809272.
   Google Scholar
• Chu, Y., B. Urguhart, S. Gohari, H. T. C. Pedro, J. Kleissl, and C. F. M. Coimbra. 2015. Short-term reforecasting of power output from a 48MWe solar PV plant. Solar Energy 112:68–77. doi:10.1016/j.solener.2014.11.017.
   Web of Science ®Google Scholar
• Das, U. K., K. Tey, M. Seyedmahmoudian, M. Idna Idris, S. Mekhilef, B. Horan, and A. Stojcevski. 2017. SVR-based model to forecast PV power generation under different weather conditions. Energies 10 (7):876. Art. no. 7. doi:10.3390/en10070876.
   Web of Science ®Google Scholar
• Davinson, R., and JG. MacKinnon. 2009. Econometric Theory and Methods. New York: Oxford University Press.
   Google Scholar
• Engle, R. F., and C. W. J. Granger. 1987. Co-integration and error correction: Representation, estimation, and testing. Essays in econometrics, 251–76. Cambridge, Massachusetts: Harvard University Press.
   Google Scholar
• Espinar, B., L. Ramirez, A. Drews, H. G. Beyer, L. F. Zarzalejo, J. Polo, and L. Martin. 2009. Analysis of different comparison parameters applied to solar radiation data from satellite and German radiometric stations. Solar Energy 83 (1):118–25. doi:10.1016/j.solener.2008.07.009.
   Web of Science ®Google Scholar
• Fanchette, Y., H. Ramenah, C. Tanougast, and M. Benne. 2020. Applying Johansen VECM cointegration approach to propose a forecast model of photovoltaic power output plant in reunion Island. AIMS Energy 8 (2):179–213. doi:10.3934/energy.2020.2.179.
   Google Scholar
• Granger, C. W. J., and A. A. Weiss. 1983. Time series analysis of error-correction. In Studies in Econometrics, Time Series, and Multivariate Statistics, ed. S. Karlin, T. Amemiya, and L. Goodman, 255–78. Cambridge, Massachusetts: Academic Press.
   Google Scholar
• Hassan, M. A., N. Bailek, K. Bouchouicha, A. Ibrahim, B. Jamil, A. Kuriqi, E. S. M. El-Kenawy, and E.-S. M. El-Kenawy. 2022. Evaluation of energy extraction of PV systems affected by environmental factors under real outdoor conditions. Theoretical and Applied Climatology 150 (1–2):715–29. doi:10.1007/s00704-022-04166-6.
   Web of Science ®Google Scholar
• Hassan, M. A., N. Bailek, K. Bouchouicha, and S. C. Nwokolo. 2021. Ultra-short-term exogenous forecasting of photovoltaic power production using genetically optimized non-linear auto-regressive recurrent neural networks. Renewable Energy 171:191–209. doi:10.1016/j.renene.2021.02.103.
   Web of Science ®Google Scholar
• Hassani, H., and M. R. Yeganegi. 2019. Sum of squared ACF and the ljung–box statistics. Physica A: Statistical Mechanics and its Applications 520:81–86. doi:10.1016/j.physa.2018.12.028.
   Web of Science ®Google Scholar
• Hassan, Q. & M. Jaszczur, and E. Przenzak. 2017. Mathematical model for the power generation from arbitrarily oriented photovoltaic panel. E3S Web of Conferences. 14. 01028. doi:10.1051/e3sconf/20171401028.
   Google Scholar
• Iheanetu, K. J. 2022. Solar photovoltaic power forecasting: A review. Sustainability 14 (24):17005. doi:10.3390/su142417005.
   Web of Science ®Google Scholar
• Jalil, A., and N. H. Rao. 2019. Chapter 8—Time series analysis. In Environmental Kuznets Curve (EKC), ed. C. Stationarity, C. Ö. B, and Ö. I, 85–99. Academic Press.
   Google Scholar
• Johansen, S. 1988. Statistical analysis of cointegration vectors. Journal of Economic Dynamics and Control 12 (2–3):231–54. doi:10.1016/0165-1889(88)90041-3.
   Web of Science ®Google Scholar
• Johansen, S. 1991. Estimation and hypothesis testing of cointegration vectors in gaussian vector autoregressive models. Econometrica 59 (6):1551–80. doi:10.2307/2938278.
   Web of Science ®Google Scholar
• Johansen, S. 1995. Likelihood-Based Inference in Cointegrated Vector Autoregressive Models. US: Oxford University Press.
   Google Scholar
• Johansen, S., and K. Juselius. 1990. Maximum likelihood estimation and inference on cointegration — With applications to the demand for money. Oxford Bulletin of Economics and Statistics 52 (2):169–210. doi:10.1111/j.1468-0084.1990.mp52002003.x.
   Web of Science ®Google Scholar
• Katircioglu, S. T. 2009. Revisiting the tourism-led-growth hypothesis for Turkey using the bounds test and Johansen approach for cointegration. Tourism Management 30 (1):17–20. doi:10.1016/j.tourman.2008.04.004.
   Web of Science ®Google Scholar
• Kumari, P., and D. Toshniwal. 2021. Deep learning models for solar irradiance forecasting: A comprehensive review. Journal of Cleaner Production 318:128566. doi:10.1016/j.jclepro.2021.128566.
   Web of Science ®Google Scholar
• Li, Y., Y. Shu, and L. Shu. 2014. An ARMAX model for forecasting the power output of a grid connected photovoltaic system. Renewable Energy 66:78–89. doi:10.1016/j.renene.2013.11.067.
   Web of Science ®Google Scholar
• Marcinkiewicz, E. 2014. Some aspects of application of VECM analysis for modeling causal relationships between spot and futures prices. Optimum Studia Ekonomiczne 71 (5(71)):114–25. doi:10.15290/ose.2014.05.71.09.
   Google Scholar
• Mills, T. C. 1983. Chapter 14—Error correction, spurious regressions, and cointegration. In Applied Time Series Analysis, ed. T. Mills, 233–53. Academic Press.
   Google Scholar
• Ministry of Energy and Public Utilities. 2019. Renewable energy roadmap 2030 for the electricity sector. Republic of Mauritius.
   Google Scholar
• Nwokolo, S. C., A. U. Obiwulu, and J. C. Ogbulezie. 2023. Machine learning and analytical model hybridization to assess the impact of climate change on solar PV energy production. Physics and Chemistry of the Earth, Parts A/B/C 130:103389. doi:10.1016/j.pce.2023.103389.
   Web of Science ®Google Scholar
• Pamela, R., and O. Vishwamitra. 2015. A hybrid method for forecasting the energy output of photovoltaic systems. Energy Conversion and Management 95:406–13. doi:10.1016/j.enconman.2015.02.052.
   Web of Science ®Google Scholar
• Pedro, H., and C. Coimbra. 2012. Assessment of forecasting techniques for solar power production with no exogenous inputs. Solar Energy 86 (7):2017–28. doi:10.1016/j.solener.2012.04.004.
   Web of Science ®Google Scholar
• Pelland, S., J. Remund, J. Kleissl, T. Oozeki, and K. De Brabandere. 2013. Photovoltaic and Solar Forecasting. State of the Art.
   Google Scholar
• Pesaran, M. H., Y. Shin, and R. J. Smith. 2001. Bounds testing approaches to the analysis of level relationships. Journal of Applied Econometrics 16 (3):289–326. doi:10.1002/jae.616.
   Web of Science ®Google Scholar
• Ramenah, H., P. Casin, M. Ba, M. Benne, and C. Tanougast. 2018. Accurate determination of parameters relationship for photovoltaic power output by augmented dickey fuller test and engle granger method. AIMS Energy 6 (1):19–48. doi:10.3934/energy.2018.1.19.
   Google Scholar
• Raschka, S., and V. Mirjalili. 2017. Python Machine Learning. Birmingham, UK: Packt Publishing Ltd.
   Google Scholar
• Şen, Z. 2004. Solar energy in progress and future research trends. Progress in Energy and Combustion Science 30 (4):367–416. doi:10.1016/j.pecs.2004.02.004.
   Web of Science ®Google Scholar
• Tajjour, S., and S. S. Chandel. 2022. Power generation forecasting of a solar photovoltaic power plant by a novel transfer learning technique with small solar radiation and power generation training data sets. SSRN Electronic Journal. Available at SSRN 4024225. doi:10.2139/ssrn.4024225.
   Google Scholar
• Tajjour, S., and S. S. Chandel. 2023. A comprehensive review on sustainable energy management systems for optimal operation of future-generation of solar microgrids. Sustainable Energy Technologies and Assessments 58:103377. doi:10.1016/j.seta.2023.103377.
   Web of Science ®Google Scholar
• Wan, C., J. Zhao, Y. Song, Z. Xu, J. Lin, and Z. Hu. 2015. Photovoltaic and solar power forecasting for smart grid energy management. CSEE Journal of Power and Energy Systems 1 (4):38–46. doi:10.17775/CSEEJPES.2015.00046.
   Google Scholar
• Yadav, A. K., and S. S. Chandel. 2017. Identification of relevant input variables for prediction of 1-minute time-step photovoltaic module power using artificial neural network and multiple linear regression models. Renewable and Sustainable Energy Reviews 77:955–69. doi:10.1016/j.rser.2016.12.029.
   Web of Science ®Google Scholar
• Yadav, A. K., V. Sharma, H. Malik, and S. S. Chandel. 2018. Daily array yield prediction of grid-interactive photovoltaic plant using relief attribute evaluator based radial basis function neural network. Renewable and Sustainable Energy Reviews 81:2115–27. doi:10.1016/j.rser.2017.06.023.
   Web of Science ®Google Scholar