Analysing the geographical impact on short-term solar irradiance forecasting using univariate and multivariate time series models

References

• Aghaei, M., A. Eskandari, S. Vaezi, and S. S. Chopra. 2020. Solar PV power plants. In Photovoltaic solar energy conversion, 313–48. Elsevier. doi:10.1016/b978-0-12-819610-6.00010-7.
   Google Scholar
• Akaike, H. 1974. A New look at the statistical model identification 215–22. doi: 10.1007/978-1-4612-1694-0_16.
   Google Scholar
• Akarslan, E., F. O. Hocaoglu, and R. Edizkan. 2018. Novel short term solar irradiance forecasting models. Renewable Energy 123:58–66. doi:10.1016/j.renene.2018.02.048.
   Web of Science ®Google Scholar
• Alsharif, M. H., M. K. Younes, and J. Kim. 2019. Time series ARIMA model for prediction of daily and monthly average global solar radiation: The case study of Seoul, South Korea. Symmetry 11 (2):240. doi:10.3390/sym11020240.
   Google Scholar
• Alves dos Santos, S. A., J. P. João, C. A. Carlos, and R. A. Marques Lameirinhas. 2021. The impact of aging of solar cells on the performance of photovoltaic panels. Energy Conversion and Management: X 10:10. doi:10.1016/j.ecmx.2021.100082.
   Google Scholar
• ArunKumar, K. E., D. V. Kalaga, C. Mohan Sai Kumar, M. Kawaji, and T. M. Brenza. 2022. Comparative analysis of gated recurrent units (GRU), long short-term memory (LSTM) cells, autoregressive Integrated moving average (ARIMA), seasonal autoregressive Integrated moving average (SARIMA) for forecasting COVID-19 trends. Alexandria Engineering Journal 61 (10):7585–603. doi:10.1016/j.aej.2022.01.011.
   Web of Science ®Google Scholar
• ArunKumar, K. E., D. V. Kalaga, C. M. Sai Kumar, G. Chilkoor, M. Kawaji, and T. M. Brenza. 2021. Forecasting the dynamics of cumulative COVID-19 cases (confirmed, recovered and deaths) for top-16 countries using statistical machine learning models: Auto-Regressive Integrated Moving average (ARIMA) and seasonal Auto-Regressive Integrated Moving average (SARIMA). Applied Soft Computing 103. doi:10.1016/j.asoc.2021.107161.
   PubMed Web of Science ®Google Scholar
• Balikci, A., B. T. Azizoǧlu, E. Durbaba, and E. Akpinar (2017). Efficiency calculation of inverter for PV applications using MATLAB and SPICE. Proceedings - 2017 International Conference on Optimization of Electrical and Electronic Equipment, OPTIM 2017 and 2017 Intl Aegean Conference on Electrical Machines and Power Electronics, ACEMP 2017, 593–98. 10.1109/OPTIM.2017.7975033
   Google Scholar
• Belmahdi, B., M. Louzazni, and A. E. Bouardi. 2020. One month-ahead forecasting of mean daily global solar radiation using time series models. Optik 219:219. doi:10.1016/j.ijleo.2020.165207.
   Web of Science ®Google Scholar
• Belmahdi, B., M. Louzazni, and A. El Bouardi. 2022. Comparative optimization of global solar radiation forecasting using machine learning and time series models. Environmental Science and Pollution Research 29 (10):14871–88. doi:10.1007/s11356-021-16760-8.
   PubMed Web of Science ®Google Scholar
• Benmouiza, K., and A. Cheknane. 2015. Small-scale solar radiation forecasting using ARMA and nonlinear autoregressive neural network models. Theoretical and Applied Climatology 124 (3):945–58. doi:10.1007/S00704-015-1469-Z.
   Web of Science ®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 (PC):226–35. doi:10.1016/j.solener.2013.10.002.
   Google Scholar
• Box, G. E., J. G. M, R. G. C, and L. G. M. 2015. Time series analysis: Forecasting and control. New York: Wiley.
   Google Scholar
• Dincer, F., and M. E. Meral. 2010. Critical factors that affecting efficiency of solar cells. Smart Grid and Renewable Energy 1 (01):47–50. doi:10.4236/sgre.2010.11007.
   Google Scholar
• Ding, C., and H. Peng. 2005. Minimum redundancy feature selection from microarray gene expression data. Journal of Bioinformatics and Computational Biology 3 (2):185–205. doi:10.1142/S0219720005001004.
   PubMedGoogle Scholar
• Drachal, K. 2021. Forecasting crude oil real prices with averaging time-varying VAR models. Resources Policy 74:102244. doi:10.1016/j.resourpol.2021.102244.
   Web of Science ®Google Scholar
• Enders, W. 2004. Applied time series econometrics, Eds. H. Lütkepohl & M. Krätzig. Cambridge University Press. doi:10.1017/CBO9780511606885.
   Google Scholar
• Fouilloy, A., C. Voyant, G. Notton, F. Motte, C. Paoli, M. L. Nivet, E. Guillot, and J. L. Duchaud. 2018. Solar irradiation prediction with machine learning: Forecasting models selection method depending on weather variability. Energy 165:620–29. doi:10.1016/j.energy.2018.09.116.
   Web of Science ®Google Scholar
• Gellert, A., U. Fiore, A. Florea, R. Chis, and F. Palmieri. 2022. Forecasting electricity consumption and production in smart homes through statistical methods. Sustainable Cities and Society 76:76. doi:10.1016/j.scs.2021.103426.
   Web of Science ®Google Scholar
• Ghimire, S., B. Bhandari, D. Casillas-Pérez, R. C. Deo, and S. Salcedo-Sanz. 2022. Hybrid deep CNN-SVR algorithm for solar radiation prediction problems in Queensland, Australia. Engineering Applications of Artificial Intelligence 112:104860. doi:10.1016/j.engappai.2022.104860.
   Web of Science ®Google Scholar
• Ghimire, S., R. C. Deo, D. Casillas-Pérez, and S. Salcedo-Sanz. 2022. Boosting solar radiation predictions with global climate models, observational predictors and hybrid deep-machine learning algorithms. Applied Energy 316:119063. doi:10.1016/j.apenergy.2022.119063.
   Web of Science ®Google Scholar
• Ghimire, S., R. C. Deo, H. Wang, M. S. Al-Musaylh, D. Casillas-Pérez, and S. Salcedo-Sanz. 2022. Stacked LSTM sequence-to-sequence autoencoder with feature selection for daily solar radiation prediction: A review and New modeling results. Energies 15 (3):1061. doi:10.3390/en15031061.
   Web of Science ®Google Scholar
• Ghimire, S., T. Nguyen-Huy, R. C. Deo, D. Casillas-Pérez, and S. Salcedo-Sanz. 2022. Efficient daily solar radiation prediction with deep learning 4-phase convolutional neural network, dual stage stacked regression and support vector machine CNN-REGST hybrid model. Sustainable Materials and Technologies 32:e00429. doi:10.1016/j.susmat.2022.e00429.
   Web of Science ®Google Scholar
• Granger, C. W. J. 1969. Investigating causal relations by Econometric models and cross-spectral methods. Econometrica 37 (3):424–38. doi:10.2307/1912791.
   Web of Science ®Google Scholar
• Granger, C. W. J. 1980. Testing for causality: A personal viewpoint. Journal of Economic Dynamics and Control 2 (C):329–52. doi:10.1016/0165-1889(80)90069-X.
   Google Scholar
• Hannah Ritchie, M. R., and P. Rosado. 2020. Energy. Our World in Data. Accessed may 26, 2022.
   Google Scholar
• Harish, A., G. Giridhar, A. Ramanan, K. Balaraman, and P. K. Das. n.d. Formulation of performance of inverters for solar photovoltaic power plants-Indian case study.
   Google Scholar
• Hosenuzzaman, M., N. A. Rahim, J. Selvaraj, and M. Hasanuzzaman. n.d. Factors affecting the pv based power generation.
   Google Scholar
• Jaihuni, M., J. K. Basak, F. Khan, F. G. Okyere, E. Arulmozhi, A. Bhujel, J. Park, L. D. Hyun, and H. T. Kim. 2020. A partially amended hybrid bi-GRU—ARIMA model (PAHM) for predicting solar irradiance in short and very-short terms. Energies 13 (2):435. doi:10.3390/EN13020435.
   Web of Science ®Google Scholar
• Khan, W., S. Walker, and W. Zeiler. 2022. Improved solar photovoltaic energy generation forecast using deep learning-based ensemble stacking approach. Energy 240:240. doi:10.1016/j.energy.2021.122812.
   Web of Science ®Google Scholar
• Konstantinou, M., S. Peratikou, and A. G. Charalambides. 2021. Solar photovoltaic forecasting of power output using lstm networks. Atmosphere 12 (1):1–17. doi:10.3390/atmos12010124.
   PubMed Web of Science ®Google Scholar
• Leamer, E. E. 1985. Vector autoregressions for causal inference? Carnegie-Rochester Conference Series on Public Policy 22 (C):283. doi:10.1016/0167-2231(85)90035-1.
   Google Scholar
• Lütkepohl, H. 2010. Impulse response function. Macroeconometrics and Time Series Analysis 145–50. doi:10.1057/9780230280830_16.
   Google Scholar
• Mohammadnia, A., A. Rezania, B. M. Ziapour, F. Sedaghati, and L. Rosendahl. 2020. Hybrid energy harvesting system to maximize power generation from solar energy. Energy Conversion and Management 205:205. doi:10.1016/j.enconman.2019.112352.
   Web of Science ®Google Scholar
• NSRDB. (n.d.). Accessed April 21, 2023. https://nsrdb.nrel.gov/data-viewer.
   Google Scholar
• Olorunfemi, B. O., O. A. Ogbolumani, and N. Nwulu. 2022. Solar panels dirt monitoring and cleaning for performance improvement: A systematic review on smart systems. Sustainability (Switzerland) 14(17): MDPI. doi:10.3390/su141710920.
   Google Scholar
• Power Sector of India. (n.d.). Accessed April 17, 2023. https://powermin.gov.in/en/content/power-sector-glance-all-india.
   Google Scholar
• Qing, X., and Y. Niu. 2018. Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM. Energy 148:461–68. doi:10.1016/j.energy.2018.01.177.
   Web of Science ®Google Scholar
• Rai, A., A. Shrivastava, and K. C. Jana. 2022. A robust auto encoder-gated recurrent unit (AE-GRU) based deep learning approach for short term solar power forecasting. Optik 252:252. doi:10.1016/j.ijleo.2021.168515.
   Web of Science ®Google Scholar
• Rathore, P. K. S., S. Rathore, R. Pratap Singh, and S. Agnihotri. 2018. Solar power utility sector in india: Challenges and opportunities. In Renewable and sustainable energy reviews, Vol. 81. 2703–13. Elsevier Ltd. doi:10.1016/j.rser.2017.06.077.
   Google Scholar
• Rodrigues, S. D., R. M. Ueda, A. C. Barreto, R. R. Zanini, and A. M. Souza. 2021. How atmospheric pollutants impact the development of chronic obstructive pulmonary disease and lung cancer: A var-based model. Environmental Pollution 275:275. doi:10.1016/j.envpol.2021.116622.
   Web of Science ®Google Scholar
• Schwarz, G. 1978. Estimating the dimension of a model. The Annals of Statistics 6 (2):6(2. doi:10.1214/aos/1176344136.
   Web of Science ®Google Scholar
• Shadab, A., S. Said, and S. Ahmad. 2019. Box–Jenkins multiplicative ARIMA modeling for prediction of solar radiation: A case study. International Journal of Energy and Water Resources 3 (4):305–18. doi:10.1007/s42108-019-00037-5.
   Google Scholar
• Silva, M., J. J. Roberts, and P. O. Prado. 2021. Calculation of the shading factors for solar modules with matlab. Energies 14 (15):4713. doi:10.3390/en14154713.
   Web of Science ®Google Scholar
• Solar Power Generation Data | Kaggle. (n.d.). Accessed July 18, 2022. https://www.kaggle.com/datasets/anikannal/solar-power-generation-data.
   Google Scholar
• Ssekulima, E. B., M. B. Anwar, A. Al Hinai, and M. S. El Moursi. 2016. Wind speed and solar irradiance forecasting techniques for enhanced renewable energy integration with the grid: A review. IET Renewable Power Generation 10 (7):885–98. Institution of Engineering and Technology. doi:10.1049/iet-rpg.2015.0477.
   Web of Science ®Google Scholar
• Yu, Y., J. Cao, and J. Zhu. 2019. An LSTM short-term solar irradiance forecasting under complicated weather conditions. IEEE Access 7:145651–66. doi:10.1109/ACCESS.2019.2946057.
   Web of Science ®Google Scholar
• Zhang, C., K. Zhou, S. Yang, and Z. Shao. 2017. Exploring the transformation and upgrading of China’s economy using electricity consumption data: A VAR–VEC based model. Physica A: Statistical Mechanics and Its Applications 473:144–55. doi:10.1016/j.physa.2017.01.004.
   Web of Science ®Google Scholar
• Zhao, Z., R. Anand, and M. Wang 2019. Maximum Relevance and Minimum Redundancy Feature Selection Methods for a Marketing Machine Learning Platform. http://arxiv.org/abs/1908.05376
   Google Scholar
• Zorrilla-Casanova, J., M. Piliougine, J. Carretero, P. Bernaola, P. Carpena, L. Mora-Lopez, and M. Sidrach-de-Cardona 2011. Analysis of dust losses in photovoltaic modules. Proceedings of the World Renewable Energy Congress –Sweden, 8–13 May, 2011, Linköping, Sweden, 57, 2985–92. 10.3384/ecp110572985
   Google Scholar