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Title: Aggregated Residential Load Modeling Using Dynamic Bayesian Networks

Abstract

Abstract—It is already obvious that the future power grid will have to address higher demand for power and energy, and to incorporate renewable resources of different energy generation patterns. Demand response (DR) schemes could successfully be used to manage and balance power supply and demand under operating conditions of the future power grid. To achieve that, more advanced tools for DR management of operations and planning are necessary that can estimate the available capacity from DR resources. In this research, a Dynamic Bayesian Network (DBN) is derived, trained, and tested that can model aggregated load of Heating, Ventilation, and Air Conditioning (HVAC) systems. DBNs can provide flexible and powerful tools for both operations and planing, due to their unique analytical capabilities. The DBN model accuracy and flexibility of use is demonstrated by testing the model under different operational scenarios.

Authors:
; ; ;
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1233490
Report Number(s):
PNNL-SA-99318
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Conference
Resource Relation:
Conference: IEEE International Conference on Smart Grid Communications (SmartGridCom 2014), November 3-6, 2014, Venice, Italy, 818-823
Country of Publication:
United States
Language:
English
Subject:
Bayesian; Smart Grid; Demand Response; Load Modeling

Citation Formats

Vlachopoulou, Maria, Chin, George, Fuller, Jason C., and Lu, Shuai. Aggregated Residential Load Modeling Using Dynamic Bayesian Networks. United States: N. p., 2014. Web. doi:10.1109/SmartGridComm.2014.7007749.
Vlachopoulou, Maria, Chin, George, Fuller, Jason C., & Lu, Shuai. Aggregated Residential Load Modeling Using Dynamic Bayesian Networks. United States. doi:10.1109/SmartGridComm.2014.7007749.
Vlachopoulou, Maria, Chin, George, Fuller, Jason C., and Lu, Shuai. Sun . "Aggregated Residential Load Modeling Using Dynamic Bayesian Networks". United States. doi:10.1109/SmartGridComm.2014.7007749.
@article{osti_1233490,
title = {Aggregated Residential Load Modeling Using Dynamic Bayesian Networks},
author = {Vlachopoulou, Maria and Chin, George and Fuller, Jason C. and Lu, Shuai},
abstractNote = {Abstract—It is already obvious that the future power grid will have to address higher demand for power and energy, and to incorporate renewable resources of different energy generation patterns. Demand response (DR) schemes could successfully be used to manage and balance power supply and demand under operating conditions of the future power grid. To achieve that, more advanced tools for DR management of operations and planning are necessary that can estimate the available capacity from DR resources. In this research, a Dynamic Bayesian Network (DBN) is derived, trained, and tested that can model aggregated load of Heating, Ventilation, and Air Conditioning (HVAC) systems. DBNs can provide flexible and powerful tools for both operations and planing, due to their unique analytical capabilities. The DBN model accuracy and flexibility of use is demonstrated by testing the model under different operational scenarios.},
doi = {10.1109/SmartGridComm.2014.7007749},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2014},
month = {9}
}

Conference:
Other availability
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