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. Hyperparameteroptimization algorithms are used to identify suitable DL models and algorithms for automatic model identification of SARIMA modelsare utilized. The three models are evaluated using a rolling origin strategy on a test dataset that consists of 10 months (January – October2023) of hourly power production. The DL models provided similarly accurate forecasts as the SARIMA model according to meansquared error and mean absolute error. However, the DL models are poorly calibrated, resulting in lower coverage compared to theSARIMA model. Furthermore, the models are using a univariate time series (i.e., using historical power production to forecast futurepower production) and future studies need to explore additional variables that may be useful in providing a more accurate forecast.