Prediction-Based Power Consumption Monitoring of Industrial Equipment Using Interpretable Data-Driven Models

Hui Xiao1; Wenshan Hu1; Hong Zhou1; Guo-Ping Liu2

2023

10.1109/TASE.2023.3242198

IEEE Transactions on Automation Science and Engineering   影响因子和分区

1558-3783

1558-3783

Efficient energy optimization and scheduling in industrial factories depend on accurate, reasonable, and real-time monitoring of equipment power consumption. However, the power prediction of industrial equipment requires a large number of process data, which could be inevitably contaminated by some imperfect data, due to the harsh environments. Monitoring or predicting equipment power consumption is usually not feasible using data-driven black-box models with imperfect data. This paper proposes a prediction-based approach for power consumption monitoring using an interpretable data-driven model. First, a data preprocessing method is used to remove outliers and fill in missing values. Then, a Volterra polynomial basis function (VPBF) model is built to predict equipment power consumption. This model decomposes power values into a series of basis functions consisting of input parameters. Moreover, to compensate for data dropouts during the power consumption monitoring process, a networked predictive monitoring system is also proposed. Finally, this paper presents two case studies based on actual production equipment in an industrial manufacturing factory. The results demonstrate that the proposed approach can achieve satisfactory monitoring accuracy and adaptation ability. Note to Practitioners-This paper is motivated by the problem of power consumption monitoring of industrial equipment in harsh production environments. A conventional solution is to predict the power consumption using data-driven black-box models, with limited feasibility and interpretability. This paper proposes a new power consumption monitoring approach, utilizing an interpretable data-driven model and a networked predictive method. This approach accurately reveals the transparent relationships between the output and input parameters. The power prediction result is consequently interpretable and more reasonable. Meanwhile, this approach actively compensates for data dropouts in the network, which can help operators real-time grasp the power consumption of equipment. Furthermore, this approach can be integrated into energy optimization systems as a basis of optimization and decision-making. Practical applications in an aluminum manufacturing company located in Guangdong Province demonstrate that this approach is feasible and applicable. Future research is to expand this approach further for energy optimization and scheduling.

Power consumption production equipment imperfect data data-driven models networked prediction

SCI ; EI

英语

其他

National Natural Science Foundation of China[62073247]

Automation & Control Systems

Automation & Control Systems

WOS:000932855600001

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

20230813626134

Decision making ; Electric power utilization ; Energy efficiency ; Forecasting ; Interactive computer systems

Energy Conservation:525.2 ; Electric Power Systems:706.1 ; Digital Computers and Systems:722.4 ; Management:912.2

IEEE

1.School of Electrical Engineering and Automation, Wuhan University, Wuhan, China
2.Department of Electronic and Electrical Engineering, Southern University of Science and Technology, Shenzhen, China

Hui Xiao,Wenshan Hu,Hong Zhou,et al. Prediction-Based Power Consumption Monitoring of Industrial Equipment Using Interpretable Data-Driven Models[J]. IEEE Transactions on Automation Science and Engineering,2023,PP(99):1-11.

Hui Xiao,Wenshan Hu,Hong Zhou,&Guo-Ping Liu.(2023).Prediction-Based Power Consumption Monitoring of Industrial Equipment Using Interpretable Data-Driven Models.IEEE Transactions on Automation Science and Engineering,PP(99),1-11.

Hui Xiao,et al."Prediction-Based Power Consumption Monitoring of Industrial Equipment Using Interpretable Data-Driven Models".IEEE Transactions on Automation Science and Engineering PP.99(2023):1-11.