Kabran, Fatma BasogluUnlu, Kamil Demirberk2026-03-052026-03-0520251303-599110.31801/cfsuasmas.1643466https://doi.org/10.31801/cfsuasmas.1643466https://hdl.handle.net/20.500.14411/11187Renewable energy offers a cost-effective, carbon-free solution for energy needs, while protecting the environment. Accurate forecasting of electricity generation from renewable sources is crucial for the efficiency of modern power grids. This study employs a univariate deep learning approach to predict daily renewable energy generation, evaluating Recurrent Neural Networks (RNNs) and Convolutional Neural Networks (CNNs) as candidate models. Five performance metrics-mean absolute error, root mean squared error, mean absolute percentage error, mean absolute scaled error and the coefficient of determination-are employed to assess the forecasting power of the algorithms. The empirical results show that CNN outperforms other models, achieving an R2 of almost 94%. This research shows that the univariate model based on historical data of electricity load generated from renewables can accurately predict day-ahead electricity load, even without meteorological data.eninfo:eu-repo/semantics/openAccessDeep LearningUnivariate ModelRenewable EnergyShort-Term Load ForecastingArticle