Prediction of solar energy forecasting by using linear and logistic regression : A review and geographical comparative analysis in Indian context
*Siddharth WadehraCorresponding authorsiddharth.wadehra20eph@iimranchi.ac.in; wadehra.siddharth@gmail.comDepartment Information Systems and Business Analytics IIM RanchiJharkhand, IndiaView full profile → , Jagadeesh Varanasijagadeesh.varanasi20eph@iimranchi.ac.in; varanasi.jagadeesh@gmail.comDepartment Information Systems and Business Analytics IIM RanchiJarkhand, IndiaView full profile → , Ambuj Anandambuj@iimranchi.ac.inDepartment Information Systems and Business Analytics IIM RanchiJharkhand, IndiaView full profile →
* Corresponding author · click or hover a name for details
- Received:
- 12 May 2020
- Published Online:
- 19 Sep 2024
- Article type:
- Research Article
- Language:
- EN
- Article no.:
- JSMS-1230
- Pages:
- 1185–1198
Abstract
Keywords
Subject Classifications
References
[1] Harendra Kumar Yadav , Yash Pal , M.M. Tripathi “Photovoltaic Pow- er Forecasting Methods in Smart Power Grid” IEEE INDICON 2015 1570186525.
[2] Hossein Sangrody , Morteza Sarailoo, Ning Zhou, Nhu Tran, Mahdi Motalleb, Elham Foruzan “Weather forecasting error in solar energy forecasting” IET Renew. Power Gener., Vol. 11 Iss. 10, pp. 1274-1280 (2017).
[3] Gensler, A., Henze, J., Sick, B., &Raabe, N. Deep Learning for solar power forecasting—An approach using AutoEn- coder and LSTM Neural Networks. In 2016 IEEE international conference on systems, man, and cybernetics (SMC) (pp. 002858-002865). IEEE (2016, October).
[4] Haupt, S. E., &Kosovic, B. Big data and machine learning for applied weather forecasts: Forecasting solar power for utility operations. In 2015 IEEE Symposium Series on Computational Intelligence (pp. 496-501). IEEE (2015, December).
[5] Khosravi, A., Koury, R. N. N., Machado, L., &Pabon, J. J. G. Prediction of hourly solar radiation in Abu Musa Island using ma- chine learning algorithms. Journal of Cleaner Production, 176, 63-75 (2018).
[4] Lauret, P., Voyant, C., Soubdhan, T., David, M., & Poggi, P. A benchmarking of machine learning techniques for solar radiation forecasting in an insular context. Solar Energy , 112, 446-457 (2015).
[5] Li, Z., Rahman, S. M., Vega, R., & Dong, B. A hierarchical ap- proach using machine learning methods in solar photovoltaic energy production forecasting Energies forecasting. 9(1), 55 (2016).
[6] M. G. Lobo and I. S´anchez, “Regional wind power forecasting based on smoothing techniques, with application to the Spanish peninsular system,” IEEE Transactions on Power Systems, vol. 27, no. 4, pp. 1990– 1997 (Nov. 2012).
[7] https://machinelearningmastery.com/logistic-regression-for-ma-chine-learning/
[8] https:// www.renewables.ninja
[9] https://www.undp.org/content/undp/en/home/sustainable-de- velopment-goals.html
[10] https://sustainabledevelopment2015.org/sdg-conceptual-frame- work
[11] Programming Exercise 2: Logistic Regression-Machine Learning Course by Andrew Ng.
[12] V. Jagadeesh, K. Venkata Subbaiah & Jyothi Varanasi, Forecasting the probability of solar power output using logistic regression algorithm, Taylor& Francis Group, Journal of Statistics and Management Systems, Vol. 23 (1),2020,1-16
[13] Voyant, C., Notton, G., Kalogirou, S., Nivet, M. L., Paoli, C., Motte, F., &Fouilloy, A. Machine learning methods for solar radiation forecasting: A review. Renewable Energy, 105, 569-582 (2017).
[14] Sharma, N., Sharma, P., Irwin, D., &Shenoy, P. Pre- dicting solar generation from weather forecasts using machine learn- ing. In 2011 IEEE international conference on smart grid communications (SmartGridComm) (pp. 528-533). IEEE (2011, October).
[15] Mompati Koorapetse & P. Kaelo (2019) An efficient hybrid conjugate gradient-based projection method for convex Mompati Koorapetse & P. Kaelo. An efficient hybrid conjugate gradient-based projec- tion method for convex constrained nonlinear monotone equations, Journal of Interdisciplinary Mathematics, 22:6, 1031-1050 (2019).



