Research Papers

On the Spatio-Temporal End-User Energy Demands of a Dense Urban Environment

[+] Author and Article Information
Krarti Ahmed

Ecole Polytechnique,
Palaiseau 91128, France

Luis E. Ortiz

Mechanical Engineering Department,
The City College of New York,
New York, NY 10031

J. E. González

NOAA-CREST Professor
ASME Fellow
Mechanical Engineering Department,
The City College of New York,
New York, NY 10031
e-mail: jgonzalezcruz@ccny.cuny.edu

1Corresponding author.

Manuscript received November 28, 2016; final manuscript received April 13, 2017; published online May 11, 2017. Assoc. Editor: Carlos F. M. Coimbra.

J. Sol. Energy Eng 139(4), 041005 (May 11, 2017) (11 pages) Paper No: SOL-16-1487; doi: 10.1115/1.4036545 History: Received November 28, 2016; Revised April 13, 2017

Buildings in major metropolitan centers face increased peak electrical load during the warm season, especially during extreme heat events. City-wide, the increased demand for space cooling can stress the grid, increasing generation costs. It is therefore imperative to better understand building energy consumption profiles at the city scale. This understanding is not only paramount for users to avoid peak demand charges but also for utilities to improve load management. This study aims to develop a city-scale energy demand forecasting tool using high resolution weather data interfaced with a single building energy model. The forecasting tool was tested in New York City (NYC) due to the availability of building morphology data. We identified 51 building archetypes, based on the building function (residential, educational, or office), the age of the building, and the land use type. The single building simulation software used is energyplus which was coupled to an urbanized weather research and forecasting (uWRF) model for weather forecast input. Individual buildings were linked to the archetypes and scaled using the building total floor area. The single building energy model is coupled to the weather model resulting in energy maps of the city. These maps provide an energy end-use profile for NYC for total and individual components including lighting, equipment and heating, ventilation, and air-conditioning (HVAC). The methodology was validated with single building energy data for a particular location, and with city-scale electric load archives, showing good agreements in both cases.

Copyright © 2017 by ASME
Your Session has timed out. Please sign back in to continue.



Grahic Jump Location
Fig. 1

Heat Index forecast for 4 am (top row) and 3 pm (bottom row) for July 21–23, 2015 (left; center; right)

Grahic Jump Location
Fig. 2

Conceptual representation of the BEP–BEM urban physics. BEP computes radiative interactions between buildings including absorption and reflection, as well as dynamical interactions with the atmosphere. BEM in turn, couples this information to account for building heat gains and losses.

Grahic Jump Location
Fig. 3

New York City map: Spatial building type distribution by borough (Manhattan, Brooklyn, Bronx, Queens, Staten Island) (Source: nycmap360.com; modified by the authors)

Grahic Jump Location
Fig. 4

Spatial distribution of buildings type in New York City (in percentage)

Grahic Jump Location
Fig. 5

Coupling uWRF and energyplus for data processing and qgis for data visualization

Grahic Jump Location
Fig. 6

CCNY Validation model for the heat wave of July 2015. Real data were obtained from the NYISO load archive for the NYC load zone (J) (Map obtained from Google Earth.).

Grahic Jump Location
Fig. 7

Validation of the city-scale model of New York City (July 20–22, 2015). City-wide demand is simulated with (bottom panel) and without (top panel) nonbuilding demand.

Grahic Jump Location
Fig. 8

Energy consumption distribution by end-use and building type for the summer period (July 1–22)

Grahic Jump Location
Fig. 9

Heat wave versus nonheat wave (July 21 versus June 13, 2015): Energy demand (W/m2)

Grahic Jump Location
Fig. 10

Difference of energy demand between a heat wave case and a nonheat wave case: zoom-in on the south of Manhattan

Grahic Jump Location
Fig. 11

Difference electricity (Right) and HVAC demand (Left) between heat wave versus nonheat wave and function of the building type

Grahic Jump Location
Fig. 12

Spatial distribution of peak energy demand by end use: top right base electricity, top left cooling, down interior lights (for June 15, 2015) for Midtown, Manhattan, NY

Grahic Jump Location
Fig. 13

Hourly energy demand: left (7 am), middle (3 pm), right (9 pm)




Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In