Research Papers

On the Climate Variability and Energy Demands for Indoor Human Comfort Levels in a Tropical-Coastal Urban Environment

[+] Author and Article Information
Rabindra Pokhrel

CUNY City College of New York,
New York, NY 10031
e-mail: r5pokhrel@gmail.com

Luis Enrique Ortiz

CUNY City College of New York,
New York, NY 10031
e-mail: luis.ortiz.uriarte@gmail.com

Nazario D. Ramírez-Beltran

Department of Industrial Engineering,
University of Puerto Rico-Mayagüez,
Mayaguez 00680, Puerto Rico
e-mail: nazario.ramirez@upr.edu

Jorge E. González

Fellow ASME
NOAA-CREST Professor of
Mechanical Engineering,
The City College of New York,
NY 10031
e-mail: jgonzalezcruz@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 January 31, 2018; final manuscript received August 1, 2018; published online October 1, 2018. Assoc. Editor: Ming Qu.

J. Sol. Energy Eng 141(3), 031002 (Oct 01, 2018) (9 pages) Paper No: SOL-18-1047; doi: 10.1115/1.4041401 History: Received January 31, 2018; Revised August 01, 2018

The main objective of this study is to identify how climate variability and urbanization influence human comfort levels in tropical-coastal urban environments. San Juan Metro Metropolitan Area (SJMA) of the island of Puerto Rico was chosen as a reference point. A new human discomfort index (HDI) based on environmental enthalpy is defined to determine the energy required to maintain indoor human comfort levels. Regression analysis shows that both temperature and HDI are good indictors to predict total electrical energy consumption. Results showed that over the past 35 years, the average environmental enthalpy have increased, resulting in the increase of average HDI with clear bias due to urbanization. Local scale weather station data show a decreasing rate of maximum cooling per capita at –11.41 kW h/years and increasing of minimum cooling per capita of 10.64 kW h/years; however, for the whole Caribbean region, an increasing trend is observed for both minimum and maximum cooling per capita. To estimate human comfort levels under extreme heat wave events conditions, an event of 2014 was identified. The analysis is complemented by simulations from the weather forecasting system (WRF) at a resolution of 1 km, forced by data from the National Center for Environmental Prediction at 250 km spatial resolution. WRF model results were evaluated against observations showing good agreement for both temperature and relative humidity (RH) and improvements. It also shows that energy per capita in urban areas during a heat wave event can increase to 21% as compared to normal day.

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Fig. 9

Climatology of heat wave event frequency (SJIA) with total energy consumption (utility)

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Fig. 8

Scatter plot for HDI and EPC per month (a), minimum temperature and HDI (b), and maximum temperature and HDI (c) for a period of 1980–2014

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Fig. 7

Time series for yearly maximum, minimum, and mean cooling for whole Caribbean region

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Fig. 6

Time series for yearly maximum, minimum, and mean cooling for SJIA. Time series for yearly maximum, minimum, and mean cooling for whole Caribbean region.

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Fig. 5

Climatology of HDI for San Juan International Airport (SJIA)

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Fig. 4

Climatology of EPC in kW h/person/month from utility records

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Fig. 3

Monthly records of total electrical energy consumption and population in millions kW h during a period of 1980–2016

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Fig. 2

Land cover land use and topography over SJMA

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Fig. 1

Weather forecasting system domain configuration

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Fig. 10

Temperature variation from Sept. 9, 2014 to Oct. 2, 2014 for NCEP, NARR, stations, and WRF output (run1)

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Fig. 11

Relative humidity variation from Sept. 29, 2014 to Oct. 2, 2014 for NCEP, NARR, stations, and WRF output

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Fig. 12

Variation of EPC per day during fourth day (left), third day (middle), and during normal day (right). Green contour levels of 13 represent urban areas; contour lines of topography are also plotted at an interval of 200.

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Fig. 13

Temperature profile for run1 (city) and run2 (no city) case

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Fig. 14

Urban heat island and increase in HDI



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