Manee V., Chebeir J., Romagnoli J., 2019, A Comparative Study of Different Deep Learning Approaches for the Prediction of Natural Gas Demand in the United States, Chemical Engineering Transactions, 74, 745-750.
In this work, the impact of network architecture on the natural gas demand and price forecasts task is evaluated. Recurrent models such as GRU (Gated Recurrent Units) and LSTM (Long Short-Term Memory networks) are investigated to verify that their impressive performance on sequence modeling tasks like audio synthesis can be transferred over to this challenging domain. The effect of data decomposition using Empirical Mode Decomposition technique is explored. Finally, the effect of the encoder-decoder architecture on model performance is evaluated in this work. The results obtained by the different approaches are then compared to identify the most appropriate model for the case studies analyzed.
