An Integrated Deep-Learning-Based Approach for Energy Consumption Prediction of Machining Systems

Meihang Zhang, Hua Zhang, Wei Yan (), Zhigang Jiang and Shuo Zhu
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Meihang Zhang: Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China
Hua Zhang: Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China
Wei Yan: Academy of Green Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China
Zhigang Jiang: Key Laboratory of Metallurgical Equipment and Control Technology, Ministry of Education, Wuhan University of Science and Technology, Wuhan 430081, China
Shuo Zhu: Precision Manufacturing Research Institute of Wuhan University of Science and Technology, Wuhan 430081, China

Sustainability, 2023, vol. 15, issue 7, 1-17

Abstract: Large and extensive manufacturing systems consume a large proportion of manufacturing energy. A key component of energy efficiency management is the accurate prediction of energy efficiency. However, the nonlinear and vibration characteristics of machining systems’ energy consumption (EC) pose a challenge to the accurate prediction of system EC. To address this challenge, an energy consumption prediction method for machining systems is presented, which is based on an improved particle swarm optimization (IPSO) algorithm to optimize long short-term memory (LSTM) neural networks. The proposed method optimizes the LSTM hyperparameters by improving the particle swarm algorithm with dynamic inertia weights (DIWPSO-LSTM), which enhances the prediction accuracy and efficiency of the model. In the experimental results, we compared several improved optimization algorithms, and the proposed method has a performance improvement of more than 30% in mean absolute error ( MAE )and mean error( ME ).

Keywords: energy consumption prediction; particle swarm optimization algorithm of dynamic inertia weights (DIWPSO); long short-term memory network (LSTM); machining systems; deep learning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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