Machine learning for accurate methane concentration predictions: short-term training, long-term results
Ran Luo, Jingyi Wang, Ian D. Gates
Abstract
Abstract Although methane emissions from Alberta’s oil and gas sector have decreased in recent years, monitoring these emissions using Continuous Emission Monitoring Systems (CEMS) can be costly. Predictive Emissions Monitoring Systems (PEMS), powered by machine learning, offer an alternative to or can supplement CEMS. However, effective machine learning models for methane emissions prediction rely heavily on the amount of training data. To address this, we compare the prediction performance of different neural network models, including Long Short-Term Memory (LSTM), Stacked LSTM, Gated Recurrent Unit (GRU), and Bidirectional LSTM (BiLSTM), using varying time intervals for training of methane concentration data from Alberta airshed stations. The results showed that the GRU model performed better with shorter datasets, whereas the LSTM and Stacked LSTM models outperformed the GRU and BiLSTM models when trained with more historical data. However, the study found that more training data did not necessarily result in significantly better prediction models.