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Title: Ensemble-based uncertainty quantification for coordination and control of thermostatically controlled loads

Abstract

This paper presents a general uncertainty quantification (UQ) framework that provides a systematic analysis of the uncertainty involved in the modeling of a control system, and helps to improve the performance of a control strategy.

Authors:
ORCiD logo [1]; ORCiD logo [1];  [1];  [1]
  1. Pacific Northwest National Laboratory, Richland, WA, USA.
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1423419
Report Number(s):
PNNL-SA-123683
Journal ID: ISSN 2330-7706
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Journal Article
Journal Name:
Journal of Control and Decision
Additional Journal Information:
Journal Volume: 5; Journal Issue: 2; Journal ID: ISSN 2330-7706
Country of Publication:
United States
Language:
English
Subject:
ensemble-based method

Citation Formats

Li, Weixuan, Lian, Jianming, Engel, Dave, and Wang, Hong. Ensemble-based uncertainty quantification for coordination and control of thermostatically controlled loads. United States: N. p., 2017. Web. doi:10.1080/23307706.2017.1353931.
Li, Weixuan, Lian, Jianming, Engel, Dave, & Wang, Hong. Ensemble-based uncertainty quantification for coordination and control of thermostatically controlled loads. United States. doi:10.1080/23307706.2017.1353931.
Li, Weixuan, Lian, Jianming, Engel, Dave, and Wang, Hong. Thu . "Ensemble-based uncertainty quantification for coordination and control of thermostatically controlled loads". United States. doi:10.1080/23307706.2017.1353931.
@article{osti_1423419,
title = {Ensemble-based uncertainty quantification for coordination and control of thermostatically controlled loads},
author = {Li, Weixuan and Lian, Jianming and Engel, Dave and Wang, Hong},
abstractNote = {This paper presents a general uncertainty quantification (UQ) framework that provides a systematic analysis of the uncertainty involved in the modeling of a control system, and helps to improve the performance of a control strategy.},
doi = {10.1080/23307706.2017.1353931},
journal = {Journal of Control and Decision},
issn = {2330-7706},
number = 2,
volume = 5,
place = {United States},
year = {2017},
month = {7}
}

Works referenced in this record:

Market-Based Coordination of Thermostatically Controlled Loads—Part I: A Mechanism Design Formulation
journal, March 2016


Equation of State Calculations by Fast Computing Machines
journal, June 1953

  • Metropolis, Nicholas; Rosenbluth, Arianna W.; Rosenbluth, Marshall N.
  • The Journal of Chemical Physics, Vol. 21, Issue 6
  • DOI: 10.1063/1.1699114

Aggregated Modeling and Control of Air Conditioning Loads for Demand Response
journal, November 2013

  • Zhang, Wei; Lian, Jianming; Chang, Chin-Yao
  • IEEE Transactions on Power Systems, Vol. 28, Issue 4, p. 4655-4664
  • DOI: 10.1109/TPWRS.2013.2266121

Data Assimilation and Inverse Methods in Terms of a Probabilistic Formulation
journal, December 1996


Market-Based Coordination of Thermostatically Controlled Loads—Part II: Unknown Parameters and Case Studies
journal, March 2016


An adaptive ANOVA-based PCKF for high-dimensional nonlinear inverse modeling
journal, February 2014


Monte Carlo sampling methods using Markov chains and their applications
journal, April 1970


Efficient Ensemble-Based Closed-Loop Production Optimization
journal, December 2009

  • Chen, Yan; Oliver, Dean S.; Zhang, Dongxiao
  • SPE Journal, Vol. 14, Issue 04
  • DOI: 10.2118/112873-PA

Prior Probabilities
journal, January 1968