scholarly journals Optimal Energy Consumption Model for Smart Grid Households With Energy Storage

2014 ◽  
Vol 8 (6) ◽  
pp. 1154-1166 ◽  
Author(s):  
Jayaprakash Rajasekharan ◽  
Visa Koivunen
Keyword(s):  
Energy Consumption ◽  
Energy Storage ◽  
Smart Grid ◽  
Optimal Energy ◽  
Energy Consumption Model ◽  
Consumption Model

Dynamic model based energy consumption optimal motion planning for high-voltage transmission line mobile robot manipulator

2020 ◽  
pp. 146441932097380
Author(s):  
Wei Jiang ◽  
Gao Cheng Ye ◽  
Dehua Zou ◽  
An Zhang ◽  
Gan Zuo ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Motion Planning ◽  
Transmission Line ◽  
High Voltage ◽  
Robot Manipulator ◽  
High Voltage Transmission Line ◽  
Optimal Energy ◽  
Energy Consumption Model ◽  
Consumption Model ◽  
Replacement Operation

Power transmission line live working robots are important equipment and useful exploration to ensure the reliable operation of high-voltage lines and they are the development trend to realizing intelligent automation power system operation and maintenance management. At present, most robots adapt lithium battery power supply and can be hoisted on line. The robot online endurance operation time is closely related to the system energy consumption. When the robot electricity of battery is insufficient, it needs to be charged offline. In order to reduce the frequency of robot hoisted on and off line as much as possible and improve the robot battery life time after it has been online, this has become a key technology which needs to be solved urgently. It is of great theoretical significance and practical application value to promote the robot overall operation efficiency. Based on the above, this paper establishes double different types manipulator energy consumption models for high-voltage transmission line damper replacement operation and drainage plate maintenance operation, through analysis and synthesis, a general energy consumption model for different tasks have been abstracted and an objective function for optimizing the robot manipulator motion energy consumption have been constructed based on the robot dynamics, thereby, GA(genetic algorithm) has been adapted, through selecting appropriate algorithm parameters, the optimal manipulator energy consumption has been solved and then it can be substituted back to the manipulator energy consumption model so as to obtain the optimal joint energy consumption motion function, based on the optimal energy consumption results, the optimal robot energy consumption motion planning has been carried out, according to the MATLAB simulation results, the energy consumption of the optimized trajectory is significantly lower than before, which can effectively reduce the frequency the robot hoisted online and offline, so as to improve the robot operation overall efficiency, at the same time, the optimal energy consumption trajectory planning method has strong versatility for different operation tasks. Finally, based on the optimal energy consumption trajectory planning, the robot drainage board tightening and damper replacement operation experiments on 220 kV power lines which verified the effectiveness and engineering practicability of the proposed method.

scholarly journals Optimal Energy Consumption Model in Transport

2014 ◽  
Vol 61 ◽  
pp. 2379-2382
Author(s):  
Alireza Naseri ◽  
Ramin Vafaeipour Sorkhabi ◽  
Arash Dalili ◽  
Masoud Naseri
Keyword(s):  
Energy Consumption ◽  
Optimal Energy ◽  
Energy Consumption Model ◽  
Consumption Model

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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