Abstract
Energy is the primary driving force in improvement of the human life cycle. All the activities for the betterment of human life are dependent on some form of energy. Conventional energy sources rely on fossil fuels which have limited reserves and we are bound to exhaust them soon. On the other hand, non-conventional/renewable energy sources are produced on a regular basis and are clean without any polluting emissions. These sources include solar, wind, hydraulic, biomass/bio gas, geothermal, tidal, etc. Solar energy is one of the primary sources in countries like India, but it does have drawbacks like high initial cost, dependency on weather, expensive storage, space requirement, etc. It is therefore imperative to create accurate solar radiation forecasting models to identify and address these issues. Forecasting models are created based on daily or hourly data and are location specific. In this work, binning based machine learning models are proposed for accurately forecasting hourly solar radiation. These models are data driven clustering based models. The clusters are identified based on geographic locations. The proposed approach also helps reduce the number of required models without compromising the high accuracy. In this work, global and diffuse solar radiation data, gathered from five geographically distinct stations from India, is analyzed. Validation of these models demonstrate increased performance. The number models required are also significantly smaller compared to the daily or hourly models.
Similar content being viewed by others
References
Ahmad R, Kumar R (2021) ”Very short-term photovoltaic (PV) power forecasting using deep learning (LSTMs). In: IEEE international conference on intelligent technologies (CONIT), pp 1-6
Alaraj M, Kumar A, Alsaidan I, Rizwan M, Jamil M (2021) Energy production forecasting from solar photovoltaic plants based on meteorological parameters for Qassim region, Saudi Arabia. IEEE Access 9:83241–83251
Aler R, Martín R, Valls JM, Galván IM (2015) A study of machine learning techniques for daily solar energy forecasting using numerical weather models. In: Camacho D, Braubach L, Venticinque S, Badica C (eds) Intelligent distributed computing VIII. Springer, Cham, pp 269–278
Álvarez-Alvarado JM, Ríos-Moreno JG, Obregón-Biosca SA, Ronquillo-Lomelí G, Ventura-Ramos E et al (2021) Hybrid techniques to predict solar radiation using support vector machine and search optimization algorithms: a review. Appl Sci 11(3):1044
Bayrakçı HC, Demircan C, Keçebaş A (2018) The development of empirical models for estimating global solar radiation on horizontal surface: A case study. Renew Sustain Energy Rev 81:2771–2782
Belaid S, Mellit A (2016) Prediction of daily and mean monthly global solar radiation using support vector machine in an arid climate. Energy Convers Manage 118:105–118
Brahma B, Wadhvani R (2020) Solar irradiance forecasting based on deep learning methodologies and multi-site data. Symmetry 12(11):1830
Chahboun S, Maaroufi M (2021) Novel comparison of machine learning techniques for predicting photovoltaic output power. Int J Renew Energy Res 11(3):1205–1214
Choudhary A, Pandey D, Bhardwaj S (2020) Artificial neural networks based solar radiation estimation using backpropagation algorithm. Int J Renew Energy Res 10(4):1566–1575
Fan J, Wang X, Wu L, Zhang F, Bai H et al (2018) New combined models for estimating daily global solar radiation based on sunshine duration in humid regions: a case study in South China. Energy Convers Manage 156:618–625
Feng Y, Gong D, Zhang Q, Jiang S, Zhao L, Cui N (2019) Evaluation of temperature-based machine learning and empirical models for predicting daily global solar radiation. Energy Convers Manage 198:111780
de Freitas Viscondi G, Alves-Souza SN (2021) Solar irradiance prediction with machine learning algorithms: A Brazilian case study on photovoltaic electricity generation. Energies 14(18):5657
Gürel AE, Ağbulut Ü, Biçen Y (2020) Assessment of machine learning, time series, response surface methodology and empirical models in prediction of global solar radiation. J Clean Prod 277:122353
Inman RH, Pedro HT, Coimbra CF (2013) Solar forecasting methods for renewable energy integration. Prog Energy Combust Sci 39(6):535–576
Jahani B, Mohammadi B (2019) A comparison between the application of empirical and ANN methods for estimation of daily global solar radiation in Iran. Theoret Appl Climatol 137(1–2):1257–1269
Jiang Y (2008) Prediction of monthly mean daily diffuse solar radiation using artificial neural networks and comparison with other empirical models. Energy Policy 36(10):3833–3837
Kashyap Y, Bansal A, Sao AK (2015) Solar radiation forecasting with multiple parameters neural networks. Renew Sustain Energy Rev 49:825–835
Khosravi A, Nunes RO, Assad MEH, Machado L (2018) Comparison of artificial intelligence methods in estimation of daily global solar radiation. J Clean Prod 194:342–358
Kim SG, Jung JY, Sim MK (2019) A two-step approach to solar power generation prediction based on weather data using machine learning. Sustainability 11(5):1501
Kim JG, Kim DH, Yoo WS, Lee JY, Kim YB (2017) Daily prediction of solar power generation based on weather forecast information in Korea. IET Renew Power Gener 11(10):1268–1273
Li G, Xie S, Wang B, Xin J, Li Y, Du S (2020) Photovoltaic power forecasting with a hybrid deep learning approach. IEEE Access 8:175871–175880
Liu Y, Zhou Y, Chen Y, Wang D, Wang Y et al (2020) Comparison of support vector machine and copula-based nonlinear quantile regression for estimating the daily diffuse solar radiation: a case study in China. Renew Energy 146:1101–1112
Long H, Zhang Z, Su Y (2014) Analysis of daily solar power prediction with data-driven approaches. Appl Energy 126:29–37
Luo X, Zhang D, Zhu X (2021) Deep learning-based forecasting of photovoltaic power generation by incorporating domain knowledge. Energy 225:120240
Massucco S, Mosaico G, Saviozzi M, Silvestro F (2019) A hybrid technique for day-ahead PV generation forecasting using clear-sky models or ensemble of artificial neural networks according to a decision tree approach. Energies 12(7):1298
Mellit A, Massi Pavan A, Ogliari E, Leva S, Lughi V (2020) Advanced methods for photovoltaic output power forecasting: a review. Appl Sci 10(2):487
Melzi FN, Touati T, Same A, Oukhellou L (2016) Hourly solar irradiance forecasting based on machine learning models. In: 15th IEEE international conference on machine learning and applications (ICMLA), pp 441-446
Mendhurwar KA, Devabhaktuni VK, Raut R (2008) Binning algorithm for accurate computer aided device modeling. In: IEEE international symposium on circuits and systems, pp 2773-2776
Mitra I, Sharma S, Kaur M, Ramanan A, Wypior M, Heinemann D (2016) Evolution of solar forecasting in India: The introduction of REMCs. In: EuroSun conference proceedings, international solar energy society, vol 1, pp 1–10
Ogliari E, Dolara A, Manzolini G, Leva S (2017) Physical and hybrid methods comparison for the day ahead PV output power forecast. Renew Energy 113:11–21
Pedregosa F, Varoquaux G, Gramfort A, Michel V, Thirion B, Grisel O et al (2019) Scikit-learn: machine learning in Python. Theoret Appl Climatol J Mach Learn. Res 137(1):1257–1269
Rodríguez F, Galarza A, Vasquez JC, Guerrero JM (2022) Using deep learning and meteorological parameters to forecast the photovoltaic generators intra-hour output power interval for smart grid control. Energy 239:122116
Son N, Jung M (2021) Analysis of meteorological factor multivariate models for medium-and long-term photovoltaic solar power forecasting using long short-term memory. Appl Sci 11(1):316
Voyant C, Notton G, Kalogirou S, Nivet ML, Paoli C et al (2017) Machine learning methods for solar radiation forecasting: a review. Renewable Energy 105:569–582
Wang G, Su Y, Shu L (2016) One-day-ahead daily power forecasting of photovoltaic systems based on partial functional linear regression models. Renew Energy 96:469–478
Zulkifly Z, Baharin KA, Gan CK (2021) Improved machine learning model selection technique for solar energy forecasting applications. Int J Renew Energy Res 11(1):308–319
Acknowledgements
Authors would like to acknowledge India Meteorological Department, Climate Research and Services, Pune, Ministry of Earth Sciences, Government of India for providing the necessary solar radiation and surface data. An appendix contains supplementary information that is not an essential part of the text itself but which may be helpful in providing a more comprehensive understanding of the research problem or it is information that is too cumbersome to be included in the body of the paper.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Munshi, A., Moharil, R.M. Binning Based Data Driven Machine Learning Models for Solar Radiation Forecasting in India. Iran J Sci Technol Trans Electr Eng (2024). https://doi.org/10.1007/s40998-024-00716-y
Received:
Accepted:
Published:
DOI: https://doi.org/10.1007/s40998-024-00716-y