Machine Learning (ML) is frequently utilized in prediction tasks; however, its applications in hydropower forecasting, particularly in forecasting hourly power production, has not been thoroughly investigated. In this paper, two Deep Learning (DL) models, namely an autoregressive neural network and Long Short-Term Memory, are compared to a seasonal autoregressive moving average (SARIMA) model to forecast the hourly power production at a hydropower station situated in Linköping, Sweden. Hyperparameter optimization algorithms are used to identify suitable DL models and algorithms for automatic model identification of SARIMA models are utilized. The three models are evaluated using a rolling origin strategy on a test dataset that consists of 10 months (January – October 2023) of hourly power production. The DL models provided similarly accurate forecasts as the SARIMA model according to mean squared error and mean absolute error. However, the DL models are poorly calibrated, resulting in lower coverage compared to the SARIMA model. Furthermore, the models are using a univariate time series (i.e., using historical power production to forecast future power production) and future studies need to explore additional variables that may be useful in providing a more accurate forecast.