Forecasting Hourly Energy Fluctuations Using Recurrent Neural Network (RNN)

Energy forecasting is currently essential due to its various benefits. Energy data analysis for forecasting requires a functional method due to the complexity of the observed data. This forecasting study used the Recurrent Neural Networks (RNN) method. Parameters used include batch size, epoch, hidden layers, loss function, and optimizer obtained from hyperparameter tuning grid search. A comparison of different normalization methods, namely min-max, and z-score, was conducted. Using min-max normalization yielded the best performance with MAPE of 3.9398%, RMSE of 0.0630, and R2 of 0.8996. In testing with z-score normalization, it showed a performance of MAPE of 10.6120%, RMSE of 0.7648, and R2 of 0.4142.