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Title: Community Energy by Design: A Simulation-Based Design Workflow Using Measured Data Clustering to Calibrate Urban Building Energy Models (UBEMs)

Abstract

This paper presents a workflow that informs urban design decisions using measured data clustering to calibrate urban building energy models. The method's goal is to support urban design in terms of form, building systems configurations, as well as influencing user behavior aspects in the built environment through a systemic analysis of measured data to develop reliable future-case design scenario energy models. Detailed data on appliance-level electricity use are employed via data clustering to calibrate a urban building energy model for the Mueller community in Austin, TX, USA. The data were collected by the Pecan Street Institute for a year in 2014 from consumers in Austin and other surrounding cities. First, collected energy data were restructured and cleaned from corrupt and/or missing information. Second, in order to identify common energy use patterns, a model-based clustering algorithm for functional data was applied. Behavioral/usage profiles are determined through clustering and translated into usage schedules and behaviors. As a result, an urban building energy model built in the urban modeling interface (umi) was calibrated, with fully calibrated and semi-calibrated buildings, within a maximum calibration error margin of 14% for daily-scale averages. Finally, an illustration of calibrated-urban building energy model design case scenarios is presented,more » and implications on community energy potential effects are discussed.« less

Authors:
 [1]; ORCiD logo [2]
  1. Georgia Institute of Technology
  2. National Renewable Energy Laboratory (NREL), Golden, CO (United States)
Publication Date:
Research Org.:
National Renewable Energy Lab. (NREL), Golden, CO (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE)
OSTI Identifier:
1573201
Report Number(s):
NREL/JA-5500-75307
DOE Contract Number:  
AC36-08GO28308
Resource Type:
Journal Article
Journal Name:
Environment and Planning B: Urban Analytics and City Science
Additional Journal Information:
Journal Volume: 46; Journal Issue: 8
Country of Publication:
United States
Language:
English
Subject:
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION; urban design; urban simulation; model calibration; measured data; data clustering

Citation Formats

Rakha, Tarek, and El Kontar, Rawad. Community Energy by Design: A Simulation-Based Design Workflow Using Measured Data Clustering to Calibrate Urban Building Energy Models (UBEMs). United States: N. p., 2019. Web. doi:10.1177%2F2399808319841909.
Rakha, Tarek, & El Kontar, Rawad. Community Energy by Design: A Simulation-Based Design Workflow Using Measured Data Clustering to Calibrate Urban Building Energy Models (UBEMs). United States. doi:10.1177%2F2399808319841909.
Rakha, Tarek, and El Kontar, Rawad. Sat . "Community Energy by Design: A Simulation-Based Design Workflow Using Measured Data Clustering to Calibrate Urban Building Energy Models (UBEMs)". United States. doi:10.1177%2F2399808319841909.
@article{osti_1573201,
title = {Community Energy by Design: A Simulation-Based Design Workflow Using Measured Data Clustering to Calibrate Urban Building Energy Models (UBEMs)},
author = {Rakha, Tarek and El Kontar, Rawad},
abstractNote = {This paper presents a workflow that informs urban design decisions using measured data clustering to calibrate urban building energy models. The method's goal is to support urban design in terms of form, building systems configurations, as well as influencing user behavior aspects in the built environment through a systemic analysis of measured data to develop reliable future-case design scenario energy models. Detailed data on appliance-level electricity use are employed via data clustering to calibrate a urban building energy model for the Mueller community in Austin, TX, USA. The data were collected by the Pecan Street Institute for a year in 2014 from consumers in Austin and other surrounding cities. First, collected energy data were restructured and cleaned from corrupt and/or missing information. Second, in order to identify common energy use patterns, a model-based clustering algorithm for functional data was applied. Behavioral/usage profiles are determined through clustering and translated into usage schedules and behaviors. As a result, an urban building energy model built in the urban modeling interface (umi) was calibrated, with fully calibrated and semi-calibrated buildings, within a maximum calibration error margin of 14% for daily-scale averages. Finally, an illustration of calibrated-urban building energy model design case scenarios is presented, and implications on community energy potential effects are discussed.},
doi = {10.1177%2F2399808319841909},
journal = {Environment and Planning B: Urban Analytics and City Science},
number = 8,
volume = 46,
place = {United States},
year = {2019},
month = {9}
}