**2. Study Area and Data**

The borough of Manhattan, situated in the northwest part of NYC, was chosen as the study area (Figure 1). Manhattan is one of the most urbanized and populated boroughs in NYC, with a population of 1.6 million in 2017 and a geographic area of 59.13 km2. Manhattan features a humid subtropical climate, the winter is cold and damp, and the summer is warm to hot and humid. Manhattan has been classified as Climate Zone 4A by the ASHRAE. Manhattan also suffers the urban heat island effect due to a high building density and little vegetation cover. The temperature difference between Manhattan and the surrounding areas could be up to 15 ◦C. Manhattan is mainly covered by commercial buildings (e.g., offices, retails, restaurants, hotels) and residential buildings (e.g., apartments and houses), and they are contributing around 70% of the energy use in Manhattan.

**Figure 1.** Study area: Manhattan, New York City, US.

To model the building energy use for the borough of Manhattan, several GIS data were collected from the New York City Open Data platform and the State of New York government website. Specifically, the GIS building footprint data, which include all city-wide building information, was collected from the New York Open Data Platform (https://opendata.cityofnewyork.us/). The building footprint data include accurate information about building construction year, number of floors, and building location. In addition, the land-use parcel data was obtained from the website of the State of New York government (http://gis.ny.gov/parcels/). It includes information about building type and building HVAC information. For model calibration and simulation purposes, the actual weather data at Central Park, Manhattan, in 2009 and 2012 were obtained from the Whitebox technique [43]. To calibrate the new model, the Residential Energy Consumption Survey (RECS) [44] and Commercial Buildings Energy Consumption Survey (CBECS) [45] in 2009 and 2012 were collected from the Energy Information Administration (EIA), respectively.

## **3. Methodology**

In order to quantify the building energy use in Manhattan, NYC, a new building energy-use model was constructed (Figure 2); it could be detailed using the following steps. Firstly, this study generated localized weather data based on existing hourly weather data and localized physical parameters using the Urban Weather Generator (UWG). Secondly, the UBEM was employed and calibrated for modeling the building energy use of Manhattan. Finally, the building energy consumption of Manhattan was simulated using the calibrated UBEM model and the localized weather data.

**Figure 2.** Flow chart of the proposed study.

#### *3.1. Generating Localized Weather Data*

The UWG is developed by Bueno et al. [42], and it can estimate hourly air temperature with consideration of the local microclimate based on the collected hourly weather data situated outside the city. It composes four modules: the rural station model, vertical diffusion model, the urban boundary layer model, and the urban canopy and building energy model. In UWG, the user can identify and describe an urban area geometrically through the average building height, horizontal building intensity, and vertical to horizontal urban area ratio. In general, users need to input the parameters into the UWG in four categories: geometric and local parameters, radiative parameters, thermal parameters, and building model parameters. Currently, the UWG has been updated to version 4.1 and could be requested from the website of the building technology program at MIT directly (https://urbanmicroclimate.scripts.mit.edu/uwg.php).

## *3.2. Modelling Building Energy Use*

In this study, an urban building energy-use model (UBEM) was developed and used to model the building energy use of Manhattan. The UBEM modelled the city-wide building energy use by combing the building floor area, number of floors, and the modelled building energy-use intensity [12].

$$Eell\_{\dot{j}} = Eell\_m \times FA\_{\dot{j}} \times NF\_{\dot{j}} \tag{1}$$

where *EUj* is the simulated electricity or gas consumption for building *j*, *EUIm* is the electricity or gas use intensity for building type m, *FAj* is the footprint area of building *j*, and *NFj* is the total amount of floors of building *j*.

The floor areas of the buildings were derived from the GIS database of the building footprints. To simulate building energy-use intensity, this study aggregated all buildings within the study area into twenty-eight commercial and six residential building prototypes [46,47] (Figure 3). Specifically, commercial buildings were categorized as hotel, primary school, secondary school, shopping mall, warehouse, retail store, supermarket, office, hospital, quick service restaurant, and full-service restaurant. They were further categorized based on the built year (pre-1980 or post-1980) as varied energy consumption for buildings constructed before and after 1980. Moreover, office buildings were reclassified as large (>5110 m2), medium (511–5110 m2), and small offices (<511 m2), based on the floor areas; hotel buildings were regrouped into two classes: large and small hotels, based on floor areas larger or smaller than 5110 m2. Residential buildings were classified as mid-rise apartments, single-family, and multiple-family. The mid-rise apartments were further subdivided into two classes based on the built year before or after 1980. Single-family and multi-family were subdivided into four types based on the primary heating methods: electrical heating or gas heating.

**Figure 3.** Building typology of the proposed study.

An engineering model, EnergyPlus v9.2, was employed to estimate the energy-use intensity for each building prototype. To establish the energy-use intensity model for each building prototype, the reference commercial and residential building models from the Department of Energy (DOE) and the Pacific Northwest National Laboratory (PNNL) were collected and used in this study. The commercial building models were developed by the DOE directly, and it covers different climate zones [47]. The residential building models were developed by the PNNL and governed by the DOE; the models

are available for each state [46]. Specifically, the energy-use intensity model of the 28 commercial buildings and the mid-rise apartment buildings were obtained from the designed models of Baltimore developed by the DOE as NYC is situated in the same climate zone as Baltimore, and they also share similar building constructions. In addition, four residential building energy-use intensity models were collected from the designed models of NYC developed by the PNNL.

The thirty-four building energy-use models that were designed were calibrated for simulating the energy use of Manhattan. Model calibration is the key for energy-use simulation: Without calibration, the collected models may not be good enough for reflecting the actual building energy use as a discrepancy may exist between the collected models and the actual practical operation. We first updated most of the local parameters, such as latitude, longitude, and elevation, in the models, and then we calibrated the models through adjusting the buildings' internal information, such as lighting intensity, electric equipment consumption intensity, and occupancy schedule, to minimize the difference between the simulated results and the reference data. In this study, we calibrated all models using the US EIA's RECS and CBECS data in 2009 and 2012, respectively.

## **4. Results and Discussion**

### *4.1. Localized Weather Data Generated by UWG*

The Urban Weather Generator was developed by the MIT, with the actual weather data from weather stations in rural areas and with localized physical parameters input; the UWG can revise the actual weather data with consideration of the variations of the local environment. There are several physical parameters that need to be included in the UWG, such as location, latitude, longitude, city diameter, average building height, horizontal building density, wall construction, wall albedo, building glazing ratio, building window construction, building cooling and heating system, the surface albedo of weather station, and the vegetated faction of the weather station. The generated local weather data and the differences between the generated weather data and the weather data from the weather stations are included in Figure 4. It shows that urban temperatures are much higher than the temperature observed from the surrounding rural areas, and the differences are much higher in the summer. It is consistent with our knowledge that the urban heat island effect can increase the temperature in the downtown area, and such an increased temperature will definitely cause a much higher cooling demand for buildings in the downtown area.

**Figure 4.** Localized monthly average based on hourly temperature.
