scholarly journals Finding Resilient and Energy-saving Control Strategies in Smart Homes

2016 ◽  
Vol 83 ◽  
pp. 976-981 ◽  
Author(s):  
Stefano Marrone ◽  
Ugo Gentile
Keyword(s):  
Energy Saving ◽  
Control Strategies ◽  
Smart Homes ◽  
Energy Saving Control

Energy-saving control strategies for a ferromagnetic shape memory alloy based actuator

2016 ◽  
Vol 249 ◽  
pp. 112-121 ◽  
Author(s):  
E. Asua ◽  
J. Jugo ◽  
M. Eguiraun ◽  
A. García-Arribas ◽  
J. Feuchtwanger ◽  
...  
Keyword(s):  
Shape Memory Alloy ◽  
Shape Memory ◽  
Energy Saving ◽  
Control Strategies ◽  
Ferromagnetic Shape Memory Alloy ◽  
Ferromagnetic Shape Memory ◽  
Energy Saving Control

Classroom Lighting Energy-Saving Control System Based on Machine Vision Technology

2018 ◽  
pp. 143-149 ◽  
Author(s):  
Ruijie CHENG
Keyword(s):  
Control System ◽  
Machine Vision ◽  
Energy Saving ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Lighting Control ◽  
Lighting Energy ◽  
Neural Network Algorithm ◽  
Energy Saving Control ◽  
Model Algorithm

In order to further improve the energy efficiency of classroom lighting, a classroom lighting energy saving control system based on machine vision technology is proposed. Firstly, according to the characteristics of machine vision design technology, a quantum image storage model algorithm is proposed, and the Back Propagation neural network algorithm is used to analyze the technology, and a multi­feedback model for energy­saving control of classroom lighting is constructed. Finally, the algorithm and lighting model are simulated. The test results show that the design of this paper can achieve the optimization of the classroom lighting control system, different number of signals can comprehensively control the light and dark degree of the classroom lights, reduce the waste of resources of classroom lighting, and achieve the purpose of energy saving and emission reduction. Technology is worth further popularizing in practice.

Design of Energy-Saving Control Software Algorithms in Large Industrial Network

2014 ◽  
Vol 644-650 ◽  
pp. 828-831
Author(s):  
Wen Lai Liu
Keyword(s):  
Energy Saving ◽  
Control Network ◽  
Fuzzy Pid Control ◽  
Industrial Control ◽  
Genetic Method ◽  
Software Algorithms ◽  
Operation Process ◽  
Industrial Network ◽  
Energy Saving Control ◽  
Industrial Control Networks

In the operation process of large industrial control network, with conventional fuzzy PID control algorithm for industrial control networks energy-saving control, excessive industrial networks will aggravate machine wear of the single network, thereby reduce the effect of energy-saving for industrial network. This paper presents an approach for industrial network energy-saving control based on non-uniform data production rate. According to the relationship between the network load and loss, the loss model of industrial control network can be established. Adaptive linear genetic method is utilized to control industrial control network energy-saving load, so as to achieve energy-saving control of industrial control network. Experimental results show that the algorithm can effectively improve the energy-saving efficiency of industrial control network, and achieve satisfactory results.

Energy consumption optimization for AC drives position control

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Branislav Ftorek ◽  
Milan Saga ◽  
Pavol Orsansky ◽  
Jan Vittek ◽  
Peter Butko
Keyword(s):  
Control System ◽  
Energy Saving ◽  
Energy Savings ◽  
Position Control ◽  
Control Strategies ◽  
Content Type ◽  
Ac Drives ◽  
Energy Demands ◽  
Wide Range ◽  
Energy Consumption Optimization

Purpose The main purpose of this paper is to evaluate the two energy saving position control strategies for AC drives valid for a wide range of boundary conditions including an analysis of their energy expenses. Design/methodology/approach For energy demands analysis, the optimal energy control based on mechanical and electrical losses minimization is compared with the near-optimal one based on symmetrical trapezoidal speed profile. Both control strategies respect prescribed maneuver time and define acceleration profile for preplanned rest-to-rest maneuver. Findings Presented simulations confirm lower total energy expenditures of energy optimal control if compared with near-optimal one, but the differences are only small due to the fact that two energy saving strategies are compared. Research limitations/implications Developed overall control system consisting of energy saving profile generator, pre-compensator and position control system respecting principles of field-oriented control is capable to track precomputed state variables precisely. Practical implications Energy demands of both control strategies are verified and compared to simulations and preliminary experiments. The possibilities of energy savings were confirmed for both control strategies. Originality/value Experimental verification of designed control structure is sufficiently promising and confirmed assumed energy savings.

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