Study on Energy Consumption Prediction and Energy Management in Jilin Province Based on STIRPAT Model

2013 ◽  
Vol 281 ◽  
pp. 542-545 ◽  
Author(s):  
Zhuo Ma ◽  
Wei Liu ◽  
Lei Wang ◽  
Ping Liang Ma ◽  
Yong Xuan Wang ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Energy Management ◽  
Jilin Province ◽  
Energy Structure ◽  
Stirpat Model ◽  
The Sustainable Development ◽  
Important Means ◽  
Energy Consumption Prediction ◽  
Consumption Control ◽  
Consumption Prediction

Energy consumption control and energy management are the important guarantee for the sustainable development of economy and society in China. Take Jilin province as an example, we study the methods and practice of energy consumption peak prediction, discuss the control countermeasures of energy consumption peak and study the countermeasures of energy efficiency and energy management. The study shows that, technology advances, industry restructuring and energy structure adjustments are the important means of energy management.

scholarly journals A Novel CNC Milling Energy Consumption Prediction Method Based on Program Parsing and Parallel Neural Network

2021 ◽  
Vol 13 (24) ◽  
pp. 13918
Author(s):  
Jianhua Cao ◽  
Xuhui Xia ◽  
Lei Wang ◽  
Zelin Zhang ◽  
Xiang Liu
Keyword(s):  
Neural Network ◽  
Sustainable Development ◽  
Energy Consumption ◽  
Machine Tools ◽  
Prediction Method ◽  
Cnc Milling ◽  
Cnc Machine ◽  
The Sustainable Development ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

Accurate and rapid prediction of the energy consumption of CNC machining is an effective means to realize the lean management of CNC machine tools energy consumption as well as to achieve the sustainable development of the manufacturing industry. Aiming at the drawbacks of existing CNC milling energy consumption prediction methods in terms of efficiency and precision, a novel milling energy consumption prediction method based on program parsing and parallel neural network is proposed. Firstly, the relationship between CNC program and energy consumption of CNC machine tool is analyzed. Based on the structural characteristics of the CNC program, an automatic parsing algorithm for the CNC program is proposed. Moreover, based on the improved parallel neural network, the mapping relationship between the energy consumption parameters of each CNC instruction and the milling energy consumption is constructed. Finally, the proposed method is compared with the literature to verify the superiority of the proposed method in terms of prediction efficiency and accuracy, and the practicability of the method is verified through the case study. The proposed method lays the foundation for efficient and low-consumption process planning and energy efficiency improvement of machine tools and is conducive to the sustainable development of the environment.

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