Erten, Mustafa YasinAydilek, Hüseyin2025-01-212025-01-2120221308-5514https://dergipark.org.tr/tr/download/article-file/2364053https://dergipark.org.tr/tr/pub/umagd/issue/74185/1100957https://doi.org/10.29137/umagd.1100957https://hdl.handle.net/20.500.12587/20361Solar 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.eninfo:eu-repo/semantics/openAccessSolar power predictionregression modelslasso regressionmachine learningElectrical EngineeringElektrik MühendisliğiArticle13-33334210.29137/umagd.11009571100957