Solar Power Prediction using Regression Models

Year 2022, Volume: 14 Issue: 3, 333 - 342, 31.12.2022

Mustafa Yasin Erten Hüseyin Aydilek

https://doi.org/10.29137/umagd.1100957

Abstract

Solar power prediction is an important problem that has gained significant attention in recent years due to the increasing demand for renewable energy sources. In this paper, we present the results of using four different regression models for solar power prediction: linear regression, logistic regression, Lasso regression, and elastic regression. Our results show that all four models are able to accurately predict solar power, but Lasso regression and elastic regression outperform linear and logistic regression in terms of predicting the maximum solar power output. We also discuss the advantages and disadvantages of each model in the context of solar power prediction.

Keywords

Solar power prediction, regression models, lasso regression, machine learning

References

• [1] İzmirli Ayan S. M. (2018). Fotovoltaik Sistemin Yapay Zeka Algoritması İle Güç Tahmini, Yüksek Lisans Tezi, Kırklareli Üniversitesi, Fen Bilimleri Enstitüsü, Kırklareli.
• [2] H. Parmaksiz, A. Karafil, H. Özbey ve M. Kesler, “Farklı Eğim Açılarındaki Fotovoltaik Panellerin Elektriksel Ölçümlerin Raspberry Pi ile İzlenmesi”, Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 4, p.711-718,2016.
• [3] E. Dandıl , E. Gürgen , “Sezgisel Algoritma Tabanlı Yapay Sinir Ağları Kullanarak Fotovoltaik Panel Güç Çıkışlarının Tahmini: Karşılaştırmalı Bir Çalışma”, Bilecik Şeyh Edebali Üniversitesi, Bilecik, 2017.
• [4] G. Giebel, R. Brownsword, G. Kariniotakis, M. Denhard, and C. Draxl, “ The State-Of-The-Art in Short- Term Prediction of Wind Power A Literature Overview,” Technical Report, ANEMOS.plus, pp. 1-109, 2011.
• [5] A. Cosra, A. Crespo, J. Navarro, G. Lizcano, H. Madsen, and E. Feitosa, “A review on the young history of the wind power short-term prediction ,” Renewable and Sustainable Energy Reviews, vol. 12, no. 6, pp.1725-1744, 2008.
• [6] S. Miragedis, Y. Sarafidis, E. Georgopoulou, D. Lalas, M. Moschovits, F. Karagiannis, and D.Papakonstantinou, “Models for mid-term electricity demand forecasting incorporating weather influences,” Energy, vol.31, no.2-3, pp.208-227, 2006.
• [7] T. Hong, J. Wilson, and J. Xie, “Long term probabilistic load forecasting and normalization with hourly information,” IEEE Transactions on Smart Grid, vol.5, no.1, pp.456-462, jan 2014.
• [8] Wan, C., Zhao, J., Song, Y., Xu, Z., Lin, J., Hu, Z.: Photovoltaic and solar power forecasting for smart grid energy management. CSEE Journal of Power and Energy Systems 1 (4), 38–46 (2015).
• [9] Mohamed Abdel- Nasser, Karar Mahmoud. 2017. Springer. Accurate photovoltaic power forecasting models using deep LSTM-RNN. Neural Comput & Applic. DOI 10.1007/s00521-017-3225-z.
• [10] Github, 2015. Understanding LSTM Networks. http://colah.github.io/posts/2015-08-Understanding-LSTMs/. Erişim Tarihi: 14.05.2019.
• [11] Topbots, 2017. Explorıng Lstms: Understandıng Basıcs. https://www.topbots.com/exploring-lstm-tutorial-part-1-recurrent-neural-network-deep-learning/ .Erişim Tarihi: 14.05.2019.
• [12] Medium, 2018. Recurrent Neural Network Nedir?. https://medium.com/@hamzaerguder/recurrent-neural-network-nedir-bdd3d0839120 . Erişim Tarihi: 16.05.2019
• [13] Garro, B.A.; Rodríguez, K.; Vázquez, R.A. Classification of DNA Microarrays Using Artificial Neural Networks and ABC Algorithm. Appl. Soft Comput. 2015, doi:10.1016/j.asoc.2015.10.002.
• [14] Pastur-Romay, L.A.; Cedrón, F.; Pazos, A.; Porto-Pazos, A.B. Deep Artificial Neural Networks and Neuromorphic Chips for Big Data Analysis: Pharmaceutical and Bioinformatics Applications. Int. J. Mol. Sci. 2016, 17, 1313.
• [15] Izgi, E.; Öztopal, A.; Yerli, B.; Kaymak, M.K.; Şahin, A.D. Short-mid-term Solar Power Prediction by Using Artificial Neural Networks. Sol. Energy 2012, 86, 725–733, doi:10.1016/j.solener.2011.11.013.
• [16] Ashraf, I.; Chandra, a Artificial Neural Network Based Models for Forecasting Electricity Generation of Gird Connected Solar PV Power Plant. Int. J. Glob. Energy Issues 2004, 21, 119–130.