Prediction of Heat Load in Oil and Gas Gathering Stations Based on CNN–LSTM–Attention

Under the national context of energy transition and energy conservation, accurate prediction of thermal load in oil and gas gathering and transportation stations is crucial for ensuring operational safety and reducing energy consumption. To address the limitations of traditional forecasting methods in handling the nonlinear, non-stationary, and long-term temporal dependencies of thermal load data, this paper proposes a hybrid deep learning model that integrates convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and an attention mechanism, namely the CNN–LSTM–Attention model. First, key influencing factors such as ambient temperature, return water temperature, and the previous hour’s thermal load were selected as model inputs through correlation analysis. Subsequently, a CNN was employed to extract spatial features from multi-source data, LSTM to capture temporal dependencies, and an attention mechanism to dynamically focus on critical operational nodes, thereby enhancing the model’s ability to perceive important features. The experimental results show that the proposed model performs excellently in heat load prediction, achieving a root mean square error of 5.98, a mean absolute error of 4.66, and a mean absolute percentage error of 9.66%, with an R-squared (R2) value of 0.9568. Its prediction accuracy and stability are significantly superior to those of the standalone CNN and standalone LSTM models. This study provides an effective algorithmic solution for precise thermal load forecasting in oil and gas gathering and transportation stations and offers insights for optimizing the applicability of deep learning models in industrial scenarios.