Pump Feature Construction and Electrical Energy Consumption Prediction Based on Feature Engineering and LightGBM Algorithm

Abstract

In recent years, research on improving the energy consumption ratio of pumping equipment through control algorithms has improved. However, the actual behavior of pump equipment and pump characteristic information do not always correspond, resulting in deviations between the calculated energy consumption operating point and the actual operating point. This eventually results in wasted power. To solve this problem, the data from circulating pumping equipment in a large pumping facility are analyzed, and the necessary characteristics of pumping equipment electrical energy consumption are analyzed through a subset of mechanism expansion feature engineering using the Pearson correlation coefficient algorithm. Based on this, a pump energy consumption prediction method based on LightGBM is constructed and compared with other algorithm models. To improve the generalization ability of the model, rules applicable to pump power energy consumption prediction are proposed, and the model features and processes are reduced. Based on the mechanistic model, 18 features related to electric energy consumption are selected, and 6 necessary features of pump electric energy consumption are screened by feature engineering. The experimental results show that the LightGBM regression algorithm has a significant prediction effect with R2=0.94. After the importance analysis, three features that are strongly related to pump energy consumption are finally screened out. According to the prediction results, the feature engineering dataset was selected and the pump electrical energy consumption was predicted based on the LightGBM algorithm, which can significantly reduce the problem of deviation in the prediction of the electrical energy consumption of pumping equipment.

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“…The second technique, exclusive feature bundling, groups specific features together to effectively reduce feature dimensionality. This concerted effort is geared towards diminishing feature dimensionality and subsequently enhancing prediction accuracy by significantly reducing sample processing time [39].…”

“…The pooling layer, on the other hand, serves the purpose of subsampling the convolution results, diminishing the dimensions of the convolution vector, and mitigating overfitting. Its function is outlined in Equation (39). The aggregate feature values obtained from this subsampling process are consolidated into M = (M 1 , M 2 , .…”