Research on Adaptive Hybrid Energy Consumption Model Based on Data Driven under Variable Working Conditions

As to the nonlinear and time-varying problems of the energy consumption model, this paper proposes an adaptive hybrid modeling method. Firstly, the recursive least squares algorithm with adaptive forgetting factor based on fuzzy algorithm and recursive least squares algorithm is used to identify the simplified mechanism energy consumption model, which solves the data saturation phenomenon and the weights of the “old and new” data during the online identification process and guarantees the adaptability of the mechanism model. Secondly, because there is a deviation between the identified model and the simplified mechanism energy consumption model, the deviation compensation model of mechanism model is established through kernel partial least squares algorithm and the model updating strategy with sliding window, which is used to update the deviation compensation model, and then the adaptive hybrid model is established by combining with the mechanism model identified online and updated deviation compensation model. Finally, the effectiveness, generalization and adaptability of the model are verified by the actual operating data of a single working condition and variable working conditions. And comparing with the mechanism model and the data model, The comparison results show that the adaptive hybrid model has higher calculation accuracy with adaptation.

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Energy Consumption Model of Diamond Disc Sawing Machine with Variable Material Removal Rate

2020 3rd World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM) ◽

10.1109/wcmeim52463.2020.00134 ◽

2020 ◽

Author(s):

Liqiang Luan ◽

Shuowei Bai

Keyword(s):

Energy Consumption ◽

Material Removal Rate ◽

Material Removal ◽

Removal Rate ◽

Energy Consumption Model ◽

Consumption Model ◽

Diamond Disc

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Kernel Recursive Least Squares Algorithm for Transmitter-Induced Self-Interference Cancellation

2021 IEEE 93rd Vehicular Technology Conference (VTC2021-Spring) ◽

10.1109/vtc2021-spring51267.2021.9449025 ◽

2021 ◽

Author(s):

C. Auer ◽

T. Paireder ◽

M. Huemer

Keyword(s):

Least Squares ◽

Interference Cancellation ◽

Recursive Least Squares ◽

Recursive Least Squares Algorithm ◽

Least Squares Algorithm

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Machine Learning-Based Energy System Model for Tissue Paper Machines

Processes ◽

10.3390/pr9040655 ◽

2021 ◽

Vol 9 (4) ◽

pp. 655

Author(s):

Huanhuan Zhang ◽

Jigeng Li ◽

Mengna Hong

Keyword(s):

Neural Network ◽

Energy Consumption ◽

Gradient Boosting ◽

Paper Machine ◽

Steam Consumption ◽

Tissue Paper ◽

Paper Machines ◽

Energy Consumption Model ◽

Extreme Gradient Boosting ◽

Consumption Model

With the global energy crisis and environmental pollution intensifying, tissue papermaking enterprises urgently need to save energy. The energy consumption model is essential for the energy saving of tissue paper machines. The energy consumption of tissue paper machine is very complicated, and the workload and difficulty of using the mechanism model to establish the energy consumption model of tissue paper machine are very large. Therefore, this article aims to build an empirical energy consumption model for tissue paper machines. The energy consumption of this model includes electricity consumption and steam consumption. Since the process parameters have a great influence on the energy consumption of the tissue paper machines, this study uses three methods: linear regression, artificial neural network and extreme gradient boosting tree to establish the relationship between process parameters and power consumption, and process parameters and steam consumption. Then, the best power consumption model and the best steam consumption model are selected from the models established by linear regression, artificial neural network and the extreme gradient boosting tree. Further, they are combined into the energy consumption model of the tissue paper machine. Finally, the models established by the three methods are evaluated. The experimental results show that using the empirical model for tissue paper machine energy consumption modeling is feasible. The result also indicates that the power consumption model and steam consumption model established by the extreme gradient boosting tree are better than the models established by linear regression and artificial neural network. The experimental results show that the power consumption model and steam consumption model established by the extreme gradient boosting tree are better than the models established by linear regression and artificial neural network. The mean absolute percentage error of the electricity consumption model and the steam consumption model built by the extreme gradient boosting tree is approximately 2.72 and 1.87, respectively. The root mean square errors of these two models are about 4.74 and 0.03, respectively. The result also indicates that using the empirical model for tissue paper machine energy consumption modeling is feasible, and the extreme gradient boosting tree is an efficient method for modeling energy consumption of tissue paper machines.

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