<![CDATA[Operational robustness and infrastructure safety require that stable dry gas pressure in transmission pipeline networks be maintained. Due to the highly nonlinear and time-varying behaviors of the dry gas pressure, conventional methods cannot predict its tendencies accurately. Deep learning models have recently gained good performance for time-series forecasting, however combination of Bidirectional Gated Recurrent Unit (BiGRU) and Bayesian Optimization based approach for dry gas pressure forecasting remains underexplored. Based on these, this study develops Bayesian optimization-enhanced forecasting framework for BiGRU to enhance the prediction performance. The framework is based on a Gaussian Process surrogate model and an acquisition function — in this case Expected Improvement, which guides the search for optimal hyperparameter configurations. We used operational SCADA time-series data consisting of hourly measurements (pressure, temperature, flowrate and gas composition) collected from a dry gas transmission pipeline across two annual periods. Pre-processing of data consists of dealing with missing data, Min–Max normalization and sliding window conversion. Here a multistep forecasting scenario for the next 20 hours was used, and thismultistep prediction was evaluated calculating the MAE, RMSE, and R². The optimized BiGRU performed the MAE of 11.7729 psi, RMSE of 14.7827 psi and R² of 0.9389, which enhanced the baseline model by 13.67%, 12.90%, and 1.60 percentage points respectively. These results indicate that Bayesian Optimization improves the forecasting performance of BiGRU and, at the same time, decreases the manual hyperparameter tuning efforts.]]>
