Numerical Simulation and Analysis of Solar Energy Forecasting Using Machine Learning Methodologies

Main Article Content

Somya Lavania
Bharat Bhushan Jain

Abstract

India is a developing country with a high energy demand that is hard to meet with traditional ways of making power. As of July 30, 2012, New Delhi to Kolkata were all affected by the world's biggest power outage. In the next five years, India's ability to make electricity will grow by 44%. India's need for electricity goes up as the country's population and economy grow. So, what needs to change to cut down on power outages and meet the energy needs of the future? India has decided to use renewable energy sources instead of fossil fuels because it is cheaper and better for the environment. In recent years, more PV panels have been installed because they are becoming more cost-effective as a source of renewable energy. In the meantime, more data and more powerful computers have made it possible for machine-learning algorithms to make better predictions. Machine learning and time series models can help people in the energy business make accurate predictions about how much energy solar PV panels will produce. In this study, different sites are used to compare different machine learning approaches and time series models to see which one works best. Wind energy forecasting has already had a lot of research done on it, but solar energy forecasting is just now getting more and more attention. This study gives a model for a thorough review and analysis. Power system operational planning is one of the most important things to think about right now. For the power system to work well, many different factors must be predicted as accurately as possible over different forecasting horizons. But different variables have different characteristics, and academics have come up with different ways to predict them in the literature. But putting into action and analyzing recently published forecasting models is hard because there are a lot of outside factors that interact with each other in a complicated way. To plan for renewable energy sources, it is important to come up with a smart plan. Because the power system is getting more and more complicated, it is still a work in progress to find the best way to predict these variables with the least amount of computer work.

Article Details

How to Cite
Somya Lavania, & Bharat Bhushan Jain. (2022). Numerical Simulation and Analysis of Solar Energy Forecasting Using Machine Learning Methodologies. International Journal on Recent Technologies in Mechanical and Electrical Engineering, 9(1), 08–19. https://doi.org/10.17762/ijrmee.v9i1.359
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References

Hugo T. C. Pedro and Carlos F. M. Coimbra, “Assessment of forecasting techniques for solar power production with no exogenous inputs,†Solar Energy, vol. 86, no. 7, pp. 2017–2028, 2012.

Xwegnon G. Agoua, Robin Girard, and George Kariniotakis, “Short-term spatio-temporal forecasting of photovoltaic power production,†IEEE Transactions on Sustainable Energy, vol. 9, no. 2, pp. 538–546, 2018.

Li, Yanting, Yan Su, and Lianjie Shu. "An ARMAX model for forecasting the power output of a grid connected photovoltaic system." Renewable Energy 66 (2014): 78-89.

Amarasinghe, Gihan, and Saranga Abeygunawardane. "An artificial neural network for solar power generation forecasting using weather parameters." (2018).

Kahina Dahmani, Rabah Dizene, Gilles Notton, Christophe Paoli, Cyril Voyant, and Marie L. Nivet, “Estimation of 5-min time-step data of tilted solar global irradiation using ANN (Artificial Neural Network) model,†Energy, vol. 70, pp 374–381, 2016.

Khan, Waqas, Shalika Walker, and Wim Zeiler. "Improved solar photovoltaic energy generation forecast using deep learning-based ensemble stacking approach." Energy 240 (2022): 122812.

Makbul A. M. Ramli, Ssennoga Twaha, and Yusuf A. Al-Turki, “Investigating the performance of support vector machine and artificial neural networks in predicting solar radiation on a tilted surface: Saudi Arabia case study,†Energy Conversion and Management, vol. 105, pp. 442–452, 2015.

Chen Yang, Anupam A. Thatte, and Le Xie, “Multitime-scale data-driven spatio-temporal forecast of photovoltaic generation,†IEEE Transactions of Sustainable Energy, vol. 6, no. 1, pp. 104–112, 2015.

Betul B. Ekici, “A least squares support vector machine model for prediction of the next day solar insolation for effective use of PV systems,†Measurement, vol. 50, no. 1, pp. 255–262, 2014.

Kymakis, Emmanuel, Sofoklis Kalykakis, and Thales M. Papazoglou. "Performance analysis of a grid connected photovoltaic park on the island of Crete." Energy Conversion and Management 50.3 (2009): 433-438Hugo T. C. Pedro and Carlos F. M. Coimbra, “Assessment of forecasting techniques for solar power production with no exogenous inputs,†Solar Energy, vol. 86, no. 7, pp. 2017–2028, 2012.

Fisher, Brent, et al. "Field performance modeling of Semprius CPV systems." Photovoltaic Specialist Conference (PVSC), 2014 IEEE 40th. IEEE, 2014.

Fatehi, Junaid H., and Kenneth J. Sauer. "Modeling the incidence angle dependence of photovoltaic modules in PVsyst." Photovoltaic Specialist Conference (PVSC), 2014 IEEE 40th. IEEE, 2014.

Truong, Nguyen Xuan, et al. "Grid-connected PV system design option for nearly zero energy building in reference building in Hanoi." Sustainable Energy Technologies (ICSET), 2016 IEEE International Conference on. IEEE, 2016.

Pillai, Gobind, et al. "The techno-economic feasibility of providing solar photovoltaic backup power." IEEE International Symposium on Technology and Society (ISTAS). Vol. 20. 2016.

Sidney, Shaji, Jose Thomas, and Mohan Lal Dhasan. "A standalone PV operated DC milk chiller for Indian climate zones." SÄdhanÄ 45, no. 1 (2020): 1-11.

Nathaniel S. PearreâŽ, Lukas G. Swan, “Statistical approach for improved wind speed forecasting for wind power production,†Sustainable Energy Technologies and Assessments vol. 27,pp. 180-191,April2018.

Ye Ren, P.N. Suganthan, N. Srikanth, “Ensemble methods for wind and solar power forecasting—Astate-of-the-artreview,†Renewable and Sustainable Energy Reviews vol.5 ,pp. 82–91,2015.

Shankar, Ravi, S. R. Pradhan, Kalyan Chatterjee, and Rajasi Mandal. "A comprehensive state of the art literature survey on LFC mechanism for power system." Renewable and Sustainable Energy Reviews 76 (2017): 1185-1207.

Sumit Saroha, S.K. Aggarwal, “A Review and Evaluation of Current Wind Power PredictionTechnologies,†Wseas transactions on power systems, vol.10, pp.1-12,2015..

Gielen, Dolf, Francisco Boshell, Deger Saygin, Morgan D. Bazilian, Nicholas Wagner, and Ricardo Gorini. "The role of renewable energy in the global energy transformation." Energy Strategy Reviews 24 (2019): 38-50.

Abedinia, Oveis, and Nima Amjady. "Short-term wind power prediction based on Hybrid Neural Network and chaotic shark smell optimization." international journal of precision engineering and manufacturing-green technology 2, no. 3 (2015): 245-254.

Carpinone, A., Giorgio, M., Langella, R. and Testa, A.,2015. Markov chain modeling for very-short-term wind power forecasting. Electric Power Systems Research, 122, pp.152-158.

Zeng, J. and Qiao, W., 2011, March. Support vector machine-based short-term wind power forecasting. In 2011 IEEE/PES Power Systems Conference and Exposition (pp. 1-8). IEEE

. Osório, G.J., Matias, J.C. and Catalão, J.P., 2014, August. Hybrid evolutionary-adaptive approach to predict electricity prices and wind power in the shortterm. In 2014 Power Systems Computation Conference (pp. 1-7). IEEE.

Nielsen, T.S., Madsen, H., Nielsen, H.A., Pinson, P., Kariniotakis, G., Siebert, N., Marti, I., Lange, M., Focken, U., Bremen, L.V. and Louka, G., 2016, February. Short-term wind power forecasting using advanced statistical methods.

Venayagamoorthy, G.K., Rohrig, K. and Erlich, I., 2012. One step ahead: short-term wind power forecasting and intelligent predictive control based on data analytics. IEEE Power and Energy Magazine, 10(5), pp.70-78.

Abdoos, A.A., 2016. A new intelligent method based on combination of VMD and ELM for short term wind power forecasting. Neurocomputing, 203, pp.111-120.

Xu, Q., He, D., Zhang, N., Kang, C., Xia, Q., Bai, J. and Huang, J., 2015. A short-term wind power forecasting approach with adjustment of numerical weather prediction input by data mining. IEEE Transactions on sustainable energy, 6(4), pp.1283- 1291.

Ayadi, F., Colak, I., Garip, I. and Bulbul, H.I., 2020, June. Impacts of Renewable Energy Resources in Smart Grid. In 2020 8th International Conference on Smart Grid (icSmartGrid) (pp. 183-188). IEEE.

Saidi, A., Harrouz, A., Colak, I., Kayisli, K. and Bayindir, R., 2019, December. Performance Enhancement of Hybrid Solar PV-Wind System Based on Fuzzy Power Management Strategy: A Case Study. In 2019 7th International Conference on Smart Grid (icSmartGrid) (pp. 126-131). IEEE.