Peak Power Minimization for Commercial Thermostatically Controlled Loads in Multi-Unit Grid-Interactive Efficient Buildings

2022 ◽  
pp. 1-1
Author(s):  
Syed Ahsan Raza Naqvi ◽  
Koushik Kar ◽  
Saptarshi Bhattacharya ◽  
Vikas Chandan
Keyword(s):  
Peak Power ◽  
Power Minimization ◽  
Thermostatically Controlled Loads

scholarly journals The scaling or ontogeny of human gait kinetics and walk-run transition: The implications of work vs. peak power minimization

2018 ◽  
Vol 81 ◽  
pp. 12-21 ◽  
Author(s):  
J.R. Usherwood ◽  
T.Y. Hubel ◽  
B.J.H. Smith ◽  
Z.T. Self Davies ◽  
G. Sobota
Keyword(s):  
Peak Power ◽  
Human Gait ◽  
Power Minimization

scholarly journals PkMin: Peak Power Minimization for Multi-Threaded Many-Core Applications

2020 ◽  
Vol 10 (4) ◽  
pp. 31
Author(s):  
Arka Maity ◽  
Anuj Pathania ◽  
Tulika Mitra
Keyword(s):  
Power Consumption ◽  
Peak Power ◽  
Parallel Execution ◽  
High Peak Power ◽  
Power Minimization ◽  
Power Peak ◽  
Peak Power Consumption ◽  
Spatio Temporal ◽  
Core Mapping ◽  
Many Core

Multiple multi-threaded tasks constitute a modern many-core application. An accompanying generic Directed Acyclic Graph (DAG) represents the execution precedence relationship between the tasks. The application comes with a hard deadline and high peak power consumption. Parallel execution of multiple tasks on multiple cores results in a quicker execution, but higher peak power. Peak power single-handedly determines the involved cooling costs in many-cores, while its violations could induce performance-crippling execution uncertainties. Less task parallelization, on the other hand, results in lower peak power, but a more prolonged deadline violating execution. The problem of peak power minimization in many-cores is to determine task-to-core mapping configuration in the spatio-temporal domain that minimizes the peak power consumption of an application, but ensures application still meets the deadline. All previous works on peak power minimization for many-core applications (with or without DAG) assume only single-threaded tasks. We are the first to propose a framework, called PkMin, which minimizes the peak power of many-core applications with DAG that have multi-threaded tasks. PkMin leverages the inherent convexity in the execution characteristics of multi-threaded tasks to find a configuration that satisfies the deadline, as well as minimizes peak power. Evaluation on hundreds of applications shows PkMin on average results in 49.2% lower peak power than a similar state-of-the-art framework.

scholarly journals Spatial peak power minimization for relaxed phase M-PSK MIMO directional modulation transmitter

2017 ◽  
Author(s):  
Ashkan Kalantari ◽  
Christos Tsinos ◽  
Mojtaba Soltanalian ◽  
Symeon Chatzinotas ◽  
Wing-Kin Ma ◽  
...  
Keyword(s):  
Peak Power ◽  
Power Minimization ◽  
Directional Modulation

Peak Power Minimization through Power Management Scheduling

2006 ◽  
Author(s):  
Shih-Hsu Huang ◽  
Chun-Hua Cheng ◽  
Chung-Hsin Chiang ◽  
Chia-Ming Chang
Keyword(s):  
Power Management ◽  
Peak Power ◽  
Power Minimization

scholarly journals Line Balancing and Sequencing for Peak Power Minimization

2020 ◽  
Vol 53 (2) ◽  
pp. 10411-10416
Author(s):  
Damien Lamy ◽  
Xavier Delorme ◽  
Paolo Gianessi
Keyword(s):  
Peak Power ◽  
Line Balancing ◽  
Power Minimization
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