The Research on Oilfield Power Load Forecasting Based on Deeply Integrated CNN-LSTM-Attention Model

Oilfield power load has a large scale and significant regulation potential, and accurately forecasting its load variations is crucial for tapping into demand response capabilities and participating in load control. In order to overcome the challenges faced by conventional Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks in capturing long-term dependencies and extracting spatial features, this paper introduces an oilfield power load forecasting approach that combines CNN, LSTM, and Attention mechanisms. First, using real load data from an oilfield in Jilin Province over two years, we analyze its spatiotemporal characteristics and construct a forecasting framework centered around LSTM. Next, An attention mechanism is then incorporated to optimize weight distribution and improve load time-series feature extraction. Additionally, CNN is integrated to extract spatial correlation features of the load. Based on this model, high-accuracy forecasts of day-ahead (24 hours) and short-term (7 days) loads are achieved. Experiments show that the model achieves an MAE of $\mathbf{3. 6 4 \%}$, an RMSE of $\mathbf{4. 6 8 \%}$, and an $\mathbf{R}^{2}$ of 0.9354. Compared to five common forecasting algorithms, the model’s average prediction accuracy reaches $\mathbf{9 6. 3 6 \%}$, significantly outperforming traditional forecasting methods. This provides a reliable forecasting evaluation tool for oilfield power loads to participate in grid demand response and contributes to improving the economic efficiency and stability of grid operation.