An agent based building energy consumption model

Author(s):  
D. Kara ◽  
P. Baxendale
2014 ◽  
Vol 953-954 ◽  
pp. 1561-1565
Author(s):  
Dao Kai Wu ◽  
Yu Hong Zhao ◽  
Xu Ji

Based on a building energy-consumption model, the building energy consumption from 2002 to 2011 in China was figured out. Analysis on building energy consumption trends showed that the building energy consumption would increase inevitably and drastically. Meanwhile, the analysis on green building presented its well energy-efficient performance. The results indicate that green building’s development is an urgent task in China.


2013 ◽  
Vol 689 ◽  
pp. 482-486
Author(s):  
Cui Zou ◽  
Xiu Li Liu

This paper established an urban building energy consumption model based on IPAT theory combined with ARMA method. Applying the model, the paper calculated the urban building energy consumption in different scenarios, and then predicted the amount of urban building energy consumption in China during 2011-2015. The amount of Chinese urban building energy consumption would grow rapidly during this period, and would reach about 895 million tons of standard coal in 2015. Results analysis showed that it would be necessary to promote energy- efficiency measures in urban buildings, especially in the public buildings.


2021 ◽  
Vol 13 (2) ◽  
pp. 762
Author(s):  
Liu Tian ◽  
Yongcai Li ◽  
Jun Lu ◽  
Jue Wang

High population density, dense high-rise buildings, and impervious pavements increase the vulnerability of cities, which aggravate the urban climate environment characterized by the urban heat island (UHI) effect. Cities in China provide unique information on the UHI phenomenon because they have experienced rapid urbanization and dramatic economic development, which have had a great influence on the climate in recent decades. This paper provides a review of recent research on the methods and impacts of UHI on building energy consumption, and the practical techniques that can be used to mitigate the adverse effects of UHI in China. The impact of UHI on building energy consumption depends largely on the local microclimate, the urban area features where the building is located, and the type and characteristics of the building. In the urban areas dominated by air conditioning, UHI could result in an approximately 10–16% increase in cooling energy consumption. Besides, the potential negative effects of UHI can be prevented from China in many ways, such as urban greening, cool material, water bodies, urban ventilation, etc. These strategies could have a substantial impact on the overall urban thermal environment if they can be used in the project design stage of urban planning and implemented on a large scale. Therefore, this study is useful to deepen the understanding of the physical mechanisms of UHI and provide practical approaches to fight the UHI for the urban planners, public health officials, and city decision-makers in China.


Processes ◽  
2021 ◽  
Vol 9 (4) ◽  
pp. 655
Author(s):  
Huanhuan Zhang ◽  
Jigeng Li ◽  
Mengna Hong

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.


2021 ◽  
Vol 45 ◽  
pp. 101212
Author(s):  
Shuo Chen ◽  
Guomin Zhang ◽  
Xiaobo Xia ◽  
Yixing Chen ◽  
Sujeeva Setunge ◽  
...  

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