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Research Articles

Time Series-Based Photovoltaic Power Forecasting to Optimize Grid Stability

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Pages 1379-1388 | Received 30 Jul 2021, Accepted 04 Sep 2022, Published online: 02 Nov 2022

References

  • Share of Solar & Wind Rose Despite Depressed Global Power Demand Amid Pandemic: IEA . Available: https://mercomindia.com/share-of-solar-and-wind-rose/
  • G. Shi and S.Eftekharnejad, “Impact of solar forecasting on power system planning,” IEEE, North American Power Symposium, 2016.
  • M. Salem , M.Basyoni and K.Aldhlan, “Design, sizing and implementation of a PV system for powering a living room,” Int. J. Eng. Res. Sci. vol. 3, no. 5, pp. 64–68. DOI: 10.25125/engineering-journal-IJOER-MAY-2017-23.
  • M. Abuella and B.Chowdhary, “Solar power forecasting using artificial neural networks,” North American Power Symposium (NAPS), 2015.
  • Ş. Sağlam, B. Oral, and S. Görgülü, “Measurements of meteorological parameter effects on photovoltaic energy production”, Int. J. Circuits Syst. Signal Process., vol. 9, 2015.
  • A. El Hendouzi and A.Bourouhou, “Solar photovoltaic power forecasting,” J. Electr. Comput. Eng., vol. 2020, pp. 1–21, 2020. DOI: 10.1155/2020/8819925.
  • C. Wan , et al., “Photovoltaic and solar power forecasting for smart grid energy management,” CSEE Power Energy Syst., vol. 1, no. 4, pp. 38–46, 2015. DOI: 10.17775/CSEEJPES.2015.00046.
  • O. Hassan , N.Zakzouk and A.Abdelsalam, “Novel photovoltaic empirical mathematical model based on function representation of captured figures from commercial panels datasheet,” Mathematics, vol. 10, no. 3, pp. 476, 2022. DOI: 10.3390/math10030476.
  • N. Mujeeb Khan , U.Amir Khan and M.Hamza Zafar, “Maximum power point tracking of PV system under uniform irradiance and partial shading conditions using machine learning algorithm trained by sailfish optimizer,” 4th International Conference on Energy Conservation and Efficiency (ICECE), 2021.
  • A. Tuohy , et al., “Solar forecasting: methods, challenges, and performance,” IEEE Power Energy Mag., vol. 13, no. 6, pp. 50–59, 2015., DOI: 10.1109/MPE.2015.2461351.
  • A. G. Wisyahyadi , R.Bagja Rizqullah and Y. T. K.Priyanto, “Analysis of partial shading effect on solar panel power output,” J. Phys., vol. 1726, pp. 1–12, 2020.
  • W. D. Soto , S. A.Klein and W. A.Beckman, “Improvement and validation of a model for photovoltaic array performance,” Sol. Energy, vol. 80, no. 1, pp. 78–88, 2006. DOI: 10.1016/j.solener.2005.06.010.
  • A. Prasad and M.Kay, “Prediction of solar power using near-real time satellite data,” Energies, vol. 14, no. 18, pp. 5865, 2021. DOI: 10.3390/en14185865.
  • M. K. Behera , I.Majumder and N.Nayak, “Solar photovoltaic power forecasting using optimized modified extreme learning machine technique,” Eng. Sci. Technol. an Int. J., vol. 21, no. 3, pp. 428–438, 2018. ISSN 2215-0986, 2018. DOI: 10.1016/j.jestch.2018.04.013.
  • M. Abuella and B.Chowdhury, “Solar power forecasting using artificial neural networks,” 2015 North American Power Symposium (NAPS), pp. 1–5, 2015. DOI: 10.1109/NAPS.2015.7335176.
  • X. Luo , D.Zhang and X.Zhu, “Deep learning based forecasting of photovoltaic power generation by incorporating domain knowledge,” Energy, vol. 225, pp. 120240, 2021. ISSN 0360-5442. DOI: 10.1016/j.energy.2021.120240.
  • M. Karakose and M.Baygin, “Image processing based analysis of moving shadow effects for reconfiguration in PV arrays,” Energycon 2014 – IEEE Int. Energy Conference, pp. 683–687, 2014.
  • V. P. Singh , K.Vaibhav and D. K.Chaturvedi, “Solar power forecasting modeling using soft computing approach,” Researchgate Nirma University International Conference, 2012.
  • B. Brahma and R.Wadhvani, “Solar irradiance forecasting based on deep learning methodologies and multi-site data,” Symmetry, vol. 12, no. 11, pp. 1830, Nov. 2020. DOI: 10.3390/sym12111830.
  • R. H. Inman , H. T.Pedro and C. F.Coimbra, “Solar forecasting methods for renewable energy integration,” 2013.
  • F. Battaglia , D.Cucina and M.Rizzo, “Detection and estimation of additive outliers in seasonal time series,” Comput. Stat., vol. 35, no. 3, pp. 1393–1409, 2020. DOI: 10.1007/s00180-019-00928-5.
  • L. Yang , S.Liu, S.Tsoka and L. G.Papageorgiou, “A regression tree approach using mathematical programming,” Expert Syst. Appl., vol. 78, pp. 347–357, 2017. DOI: 10.1016/j.eswa.2017.02.013.
  • B. Adetokun , J.Ojo and C.Muriithi, “Application of large-scale grid-connected solar photovoltaic system for voltage stability improvement of weak national grids,” Scientific Reports, vol. 11, pp. 11, 2021. DOI: 10.1038/s41598-021-04300-w.
  • R. Davy and J.Huang, “Optimal scheduling of storage for grid-connected PV,” Asia-Pacific Solar Research Conference, 29 Nov, Australian National University, Canberra. Australian PV Institute, 1p, 2016. http://hdl.handle.net/102.100.100/89323?index=1.
  • A. Al-Shetwi , M. A.Hannan, P.Ker, A.Jern, A.Alkahtani and P.Emeroylariffion, “Power quality assessment of grid-connected PV system in compliance with the recent integration requirements,” Electronics, vol. 9, no. 2, pp. 366, 2020., DOI: 10.3390/electronics9020366.
  • C. Monteiro , et.al., “Short-term forecasting models for photovoltaic plants: analytical versus soft-computing techniques,” Mathematical Problems in Engineering, vol. 2013, pp. 1–9, 2013. DOI: 10.1155/2013/767284.
  • A. Pareek and L.Gidwani, “Solar irradiation data measurement analysing techniques,” Proceedings of International Conference on Renewable Energy and Sustainable Environment, 2019.
  • P. Wang , R.van Westrhenen, J. F.Meirink, S.van der Veen and W.Knap, “Surface solar radiation forecasts by advecting cloud physical properties derived from Meteosat Second Generation observations,” Sol. Energy, vol. 177, pp. 47–58, 2019. DOI: 10.1016/j.solener.2018.10.073.
  • Atmospheric Effects . Available: https://www.pveducation.org/pvcdrom/properties-of-sunlight/atmospheric-effects#:∼:text=While%20the%20absorption%20by%20specific,to%20air%20molecules%20and%20dust.
  • W. Charfi , M.Chaabane, H.Mhiri and P.Bournot, “Performance evaluation of a solar photovoltaic system,” Energy Rep., vol. 4, pp. 400–406, 2018. DOI: 10.1016/j.egyr.2018.06.004.
  • Sun Position Calculator . Available: https://www.pveducation.org/pvcdrom/properties-of-sunlight/sun-position-calculator.
  • Seyyed Abolhasan Fatemi, Anthony Kuh . “Solar radiation forecasting using zenith angle.” IEEE Global Conference on Signal and Information Processing, 2013.
  • R. J. Hogan and S.Hirahara, “Effect of solar zenith angle specification in models on mean shortwave fluxes and stratospheric temperatures,” Geophys. Res. Lett., vol. 43, no. 1, pp. 482–488, 2016. DOI: 10.1002/2015GL066868.
  • V. Perraki and P.Kounavis, “Effect of temperature and radiation on the parameters of photovoltaic modules,” J. Renew. Sustain. Energy., vol. 8, no. 1, pp. 13102, 2016. DOI: 10.1063/1.4939561.
  • R. Mubarak , M.Hofmann, S.Riechelmann and G.Seckmeyer, “Comparison of modelled and measured tilted solar irradiance for photovoltaic applications,” Energies, vol. 10, no. 11, pp. 1688, 2017. DOI: 10.3390/en10111688.
  • C. Gueymard , “From global horizontal to global tilted irradiance: How accurate are solar energy engineering predictions in practice.” Conference paper, 2008.
  • F. Vignola , “GHI Correlations with DHI and DNI and the Effects of Cloudiness on one-minute Data.” Conference Paper, 2012.
  • A. Alzahrani , J. W.Kimball and C.Dagli, “Predicting solar irradiance using time series neural networks,” Procedia Comput. Sci., vol. 36, pp. 623–628, 2014. DOI: 10.1016/j.procs.2014.09.065.
  • E. C. Nwogu , I.Iwueze and V.Nlebedim, “Some tests for seasonality in time series data,” J. Mod. App. Stat. Meth, vol. 15, no. 2, pp. 382–399, 2016. DOI: 10.22237/jmasm/1478002920.
  • A. Montanes Bernal and A.Sanso, “The Dickey-Fuller test family and changes in the seasonal pattern,” Annales D'économie et de Statistique, vol. 61, no. 61, pp. 73, 2001. DOI: 10.2307/20076270.
  • P. H. Franses , “Seasonality, non-stationarity and the forecasting of monthly time series,” Int. J. Forecast., vol. 7, no. 2, pp. 199–208, 1991. DOI: 10.1016/0169-2070(91)90054-Y.
  • R. Mushtaq , “Augmented Dickey Fuller test,” SSRN J., 2011. DOI: 10.2139/ssrn.1911068.
  • C. T. Clack , “Modeling solar irradiance and solar PV power output to create a resource assessment using linear multiple multivariate regression,” J. Appl. Meteorol. Climatol., vol. 56, no. 1, pp. 109–125, 2017. DOI: 10.1175/JAMC-D-16-0175.1.

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