scholarly journals Application Calibration Based on Energy Consumption Model in Optimal Design of Green Buildings

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Feiran Xue ◽  
Jingyuan Zhao
Keyword(s):  
Sustainable Development ◽  
Life Cycle ◽  
Energy Consumption ◽  
Energy Saving ◽  
Energy Management ◽  
Green Buildings ◽  
Load Test ◽  
Energy Consumption Model ◽  
Consumption Model

Buildings will generate considerable resource and energy consumption, environmental impact, and costs throughout their entire life cycle. Life cycle assessment and life cycle cost methods have become widely used building sustainability evaluation methods internationally due to their long-term comprehensive evaluation perspectives and scientific quantitative calculation methods. In response to energy consumption management issues in green buildings, a questionnaire survey was conducted to investigate the concepts of sustainable development, green environmental protection, and energy consumption management of construction enterprise personnel. Through the survey, it can be seen that corporate executives generally have a sense of sustainable development and pay more attention to sustainable competition and the green development of enterprises. Approximately 90% of executives understand energy consumption models. Only 10% of employees do not understand, but they are acquired by the company’s frontline employees. The result of the feedback is just the opposite. Frontline workers pay little attention to the long-term development of the company. Only about 5% of employees are proficient in the energy management system, but the proportion of completely unknown is as high as 20%, indicating that the overall energy management awareness of Chinese enterprises still needs to be strengthened. Secondly, through design experiments, the energy-saving management effect of the energy consumption model was observed and analyzed. From the power saving and load test, we found that the monitoring of the energy consumption model is helpful for companies to choose more energy-efficient building circuit designs, achieving an average annual energy-saving effect of about 8%, so as to achieve the purpose of energy saving.

scholarly journals Energy consumption model of plasma spraying based on unit process life cycle inventory

2020 ◽  
Vol 9 (6) ◽  
pp. 15324-15334 ◽  
Author(s):  
Liming Wang ◽  
Xueju Ran ◽  
Yanle Li ◽  
Fangyi Li ◽  
Jing Liu ◽  
...  
Keyword(s):  
Life Cycle ◽  
Energy Consumption ◽  
Plasma Spraying ◽  
Life Cycle Inventory ◽  
Unit Process ◽  
Energy Consumption Model ◽  
Consumption Model

Product Life Cycle Energy Consumption Analysis Method Considering Remanufacturing

2011 ◽  
Vol 55-57 ◽  
pp. 729-736 ◽  
Author(s):  
Tao Liu ◽  
Hai Hong Huang ◽  
Zhi Feng Liu ◽  
Guang Fu Liu
Keyword(s):  
Life Cycle ◽  
Energy Consumption ◽  
Product Life Cycle ◽  
Analysis Method ◽  
Product Life ◽  
Scheme Optimization ◽  
Energy Consumption Analysis ◽  
Energy Consumption Model ◽  
Consumption Model ◽  
The Impact

The product life cycle energy consumption model was established considering the impact of remanufacturing on the product lifecycle, and the energy consumption quantitative method was given. In order to optimize the life of a product, a method to calculate its life cycle critical point was proposed. The energy consumption model was applied to two types of gearboxes, new and remanufactured, to compare their life cycle energy, and the energy-saving design scheme optimization was achieved.

scholarly journals Eco-Driving Assistance System: A New Way of How to Save Energy

2012 ◽  
Author(s):  
Lydie Nouveliere ◽  
Hong-Tu Luu ◽  
Saïd Mammar ◽  
Qi Cheng ◽  
Olivier Orfila
Keyword(s):  
Energy Consumption ◽  
Energy Saving ◽  
Human Machine Interface ◽  
System A ◽  
Energy Consumption Model ◽  
Driving Assistance ◽  
Machine Interface ◽  
Consumption Model ◽  
Save Energy ◽  
Driving Assistance System

The work developed in this paper is realized within the ecoDriver EU FP7 project that is shortly presented in the first part of the paper. Among the several main objectives underlined by this project, one consists in developing a vehicle and energy consumption model to be validated and then used to help the driver to better drive in terms of consumption and safety, advised by a HMI (Human-Machine Interface) module. Several experimental results are shown to illustrate the obtained energy saving with such an EDAS and the legal speed respect.

scholarly journals Machine Learning-Based Energy System Model for Tissue Paper Machines

Processes ◽  
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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