Assessment of LSTM and GRU Models to Predict the Electricity Production from Biogas in a Wastewater Treatment Plant

Pedro Oliveira, Francisco Supino Marcondes, M. Salomé Duarte, Dalila Durães, Gilberto Martins, Paulo Novais

Abstract: Over the decades, we have faced escalating global energy consumption and its consequential environmental impacts, including climate change and pollution. This study explicitly evaluates the use of Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) models for predicting electricity production from the biogas produced in a Wastewater Treatment Plant (WWTP) in Portugal. WWTPs play an essential role regarding environmental sustainability, namely the potential of biogas in mitigating energy consumption’s environmental impact. Also, the work details a comparison between the LSTM and GRU model’s performance, applying a grid-search methodology for hyperparameter optimization. The study employs the Root Mean Squared Error (RMSE) as an evaluation metric and uses the sliding window method to transform the problem into a supervised one. After several experiments, the results demonstrate that the LSTM-based model outperforms GRU-based models, achieving an RMSE of 347.9 kWh.

External IDs:dblp:conf/worldcist/OliveiraMDDMN24