Robust Energy-Efficient Beamforming in MISO Networks with Dynamic Energy Consumption Model

2019 ◽  
Author(s):  
Yang Lu ◽  
Ke Xiong ◽  
Lan Zhang ◽  
Pingyi Fan ◽  
Zhangdui Zhong
Keyword(s):  
Energy Consumption ◽  
Energy Efficient ◽  
Energy Consumption Model ◽  
Consumption Model

The Urgency of Green Building’s Development from the Angle of Building Energy Consumption in China

2014 ◽  
Vol 953-954 ◽  
pp. 1561-1565
Author(s):  
Dao Kai Wu ◽  
Yu Hong Zhao ◽  
Xu Ji
Keyword(s):  
Energy Consumption ◽  
Energy Efficient ◽  
Green Building ◽  
Building Energy ◽  
Building Energy Consumption ◽  
Efficient Performance ◽  
Urgent Task ◽  
Energy Consumption Model ◽  
Consumption Model

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.

Qos Constraints-Based Energy-Efficient Model for IP Networks

2013 ◽  
Vol 765-767 ◽  
pp. 1747-1751
Author(s):  
Ding De Jiang ◽  
Wen Juan Wang ◽  
Wei Han Zhang ◽  
Peng Zhang ◽  
Ya Li
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Energy Efficient ◽  
Large Scale ◽  
Ip Networks ◽  
Speed Scaling ◽  
Model Based ◽  
Energy Consumption Model ◽  
Consumption Model ◽  
Simulation Results

This paper proposes an energy-efficient model to overcome the energy-efficient problem in large-scale IP networks, based on QoS constraints. To characterize network energy consumption, we present a link energy consumption model based on the sleep and speed scaling mechanisms. If there is no traffic on a link, let it sleep, or activate it and divide its energy consumption into base energy consumption and traffic energy consumption. And then according to the link energy consumption model, we can build our energy-efficient model to improve the network energy efficiency. Finally, simulation results show that our model can significantly improve the network energy efficiency.

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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