Predictive model of pump energy consumption based on image feature extraction and instance-based transfer learning

The energy consumption of pumps for water supply accounts for a high proportion of the total global energy consumption. The analysis of the pump's energy consumption factors and the construction of prediction models play a crucial role in carbon emission reductions throughout the water supply process. However, difficulties in accurately constructing input features under complex variables and the scarcity and anomalies of monitoring data during system operation pose great challenges to accurate energy consumption prediction. In order to address this issue, a water pump energy consumption prediction method based on image feature extraction and instance transfer is proposed, using the water pump systems installed in two residential communities as the research objects. Firstly, a novel image-based correlation determination method for feature engineering was introduced and validated on a sufficient dataset. Subsequently, in the context of poor data transfer learning, a water pump energy consumption prediction model, named Two-stage TrAdaBoost.R2 (TSTR), was established by incorporating the image correlation determination method and fine-tuning the model parameters using the particle swarm optimization algorithm to enhance prediction accuracy. The experimental results demonstrated the effectiveness of the proposed image-based correlation determination method, showing a 5.49%–44.33% improvement in the coefficient of variation of root mean square error (CV-RMSE) prediction accuracy compared to classical time-series correlation determination methods. Furthermore, in the poor data transfer prediction experiment, the TSTR-based model achieved 0.90% and 20.22% higher CV-RMSE prediction accuracy compared to two comparative models. These findings confirmed the effectiveness of instance transfer learning methods in poor data scenarios and provided valuable insights for selecting input feature construction methods under complex variables.