# Energy Consumption Prediction for Drilling Pumps Based on a Long Short-Term Memory Attention Method
## 3 Results and Discussion
### Experiments and Analysis  
Based on this model, we predicted the drilling pump energy consumption under four different drilling conditions, and the test results are shown in Figure 6. The four experimental results illustrate the model's performance in predicting drilling pump power consumption under various time periods and working conditions. By comparing the actual data (blue dots) with the predicted values (orange line) across different experiments, we can conduct an in-depth analysis from several perspectives, including overall trend prediction,  
Figure 6: Pump power prediction experiments: (**a**) Test 1; (**b**) Test 2; (**c**) Test 3; (**d**) Test 4. Figure 7: Trends and fluctuations in Test 1. As shown in Figures 7-10, the actual and predicted trend lines in the image indicate that the model can capture the major changes in electricity at different times, where predicted values closely follow the fluctuations of actual data. This predictive capability demonstrates the model's ability to capture long-term trends effectively. The shaded area represents the 95% confidence interval. The model's predicted trend mostly falls within the confidence interval of the actual trend, indicating a certain level of reliability and accuracy. In the figure, the measurement numbers (instead of time) are sorted and plotted, providing a clearer perspective for observing the subtle differences between the predicted and actual trend lines. Figure 8: Trends and fluctuations in Test 2. Figure 9: Trends and fluctuations in Test 3.  
data. The predicted data and actual data show close consistency in trends, especially where significant fluctuations occur. Firstly, as shown in Figures 6(a)-c, 6(a)-c, 6(a)-c and 6(a)-c, the confidence intervals for the actual and predicted data in these figures cover the 95% stable range of each dataset, and the trend lines plotted based on these intervals illustrate the long-term trends in the data. By comparing the positions and shapes of the actual and predicted trend lines, we can assess the model's accuracy and consistency in capturing long-term trends. These trends provide insights into the overall trajectory of energy consumption, which is essential for long-term energy planning and optimization. Secondly, as shown in Figures 6(d), 6(d), 6(d) and 6(d), to better evaluate the model's performance in capturing short-term fluctuations, we sorted the actual power data as a baseline. By comparing the relative positions of the predicted power values against this baseline, we can directly analyze the model's responsiveness to minor fluctuations and rapid changes. This capability is crucial for real-time monitoring and anomaly detection in practical applications, as it enables us to identify short-term variations in the system and adjust operational strategies accordingly. **Overall Trend Prediction**. The results from all four experiments indicate that the model can accurately capture the overall trend of actual pump power fluctuations. In each experiment, the overall fluctuation trend of the predicted values (orange line) closely aligns with the actual data (blue dots). The model effectively follows the changes in actual data, whether during periods of increasing or decreasing power. This demonstrates the model's strong performance in recognizing the long-term trends and patterns of pump power variations. Figure 6(a): In the 0-1400 s time range, pump power exhibits frequent upward and downward fluctuations, ranging between 3950 kW and 4150 kW. The predicted values closely follow the actual data, especially during the 600-1000 s interval, where the model accurately predicts both the rise and fall in power. This demonstrates the model's ability to adapt to complex fluctuation patterns during this period, capturing the peaks and troughs with high precision. Figure 7(b): The time range is extended to 1600 s. Compared to Experiment 1, the power fluctuations are relatively moderate, ranging between 3950 kW and 4100 kW.