Research Papers

Forecasting Building Energy Demands With a Coupled Weather-Building Energy Model in a Dense Urban Environment

[+] Author and Article Information
Luis E. Ortiz

Mechanical Engineering Department,
The City College of New York,
160 Convent Avenue,
New York, NY 10031
e-mail: lortiz10@citymail.cuny.edu

Jorge E. Gonzalez

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

Estatio Gutierrez

Mechanical Engineering Department,
The City College of New York,
160 Convent Avenue,
New York, NY 10031
e-mail: estatio@yahoo.com

Mark Arend

Electrical Engineering Department,
The City College of New York,
160 Convent Avenue,
New York, NY 10031
e-mail: marend@ccny.cuny.edu

1Corresponding author.

Contributed by the Solar Energy Division of ASME for publication in the JOURNAL OF SOLAR ENERGY ENGINEERING: INCLUDING WIND ENERGY AND BUILDING ENERGY CONSERVATION. Manuscript received April 29, 2016; final manuscript received September 14, 2016; published online November 10, 2016. Assoc. Editor: Patrick E. Phelan.

J. Sol. Energy Eng 139(1), 011002 (Nov 10, 2016) (8 pages) Paper No: SOL-16-1197; doi: 10.1115/1.4034909 History: Received April 29, 2016; Revised September 14, 2016

Major new metropolitan centers experience challenges during management of peak electrical loads, typically occurring during extreme summer events. These peak loads expose the reliability of the electrical grid on the production and transmission side, while customers may incur considerable charges from increased metered peak demand, failing to meet demand response program obligations, or both. These challenges create a need for analytical tools that can inform building managers and utilities about near future conditions so they are better able to avoid peak demand charges and reduce building operational costs. In this article, we report on a tool and methodology to forecast peak loads at the city scale using New York City (NYC) as a test case. The city of New York experiences peak electric demand loads that reach up to 11 GW during the summertime, and are projected to increase to over 12 GW by 2025, as reported by the New York Independent System Operator (NYISO). The energy forecast is based on the Weather Research and Forecast (WRF) model version 3.5, coupled with a multilayer building energy model (BEM). Urban morphology parameters are assimilated from the New York Primary Land Use Tax-Lot Output (PLUTO), while the weather component of the model is initialized daily from the North American Mesoscale (NAM) model. A city-scale analysis is centered in the summer months of June–July 2015 which included an extreme heat event (i.e., heat wave). The 24-h city-scale weather and energy forecasts show good agreement with the archived data from both weather stations records and energy records by NYISO. This work also presents an exploration of space cooling savings from the use of white roofs as an application of the city-scale energy demand model.

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Fig. 1

PLUTO building area fraction and building height regridded at a spatial resolution of 1 km

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Fig. 2

Urban land use categories derived from the PLUTO dataset for use in the forecast over the New York metropolitan area

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Fig. 3

Baseline city-wide load derived from the NYISO load data

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Fig. 4

Outer (d01) and nested domains (d02, d03) used by the urbanized model

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Fig. 5

Weather station locations (left panel) used for model validation, time series (center panel), and their corresponding box plots (right panel)

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Fig. 6

Root mean square error (left series) and mean absolute error (right series) between model and weather stations

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Fig. 7

Time series of forecast (dashed line) and NYISO reported load for Zone J (NYC) during weekdays

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Fig. 8

Distribution of forecast and NYISO loads

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Fig. 9

Peak HVAC demand for the standard configuration (left panel) and a white roof installation on all roofs (center panel). The savings in energy are calculated for the same day (right panel).




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