1. Introduction
In the past decades, the world has experienced rapid economic development, population growth, and urban sprawl [
1]. So far, over 55% of the world’s population lives in cities, and it is predicted that 2.5 billion more people will be dwelling in cities by 2050 [
2]. This rapid urbanization has brought several challenging issues, such as the significant increases in energy consumption and CO
2 emissions [
1]. Scholars project that urban energy consumption will be over 20,000 Mtoe (million tons of oil equivalent) by 2050, which, in turn, would result in the shortage of energy and environmental degradation [
3]. In response to these challenging issues, city governments throughout the whole world have proposed ambitious greenhouse gas emission reduction plans. For instance, the City of San Francisco and the City of London have set the emission reduction target at 40% and 60% by 2025, respectively [
4]. The City of Boston proposed a Greenovate Climate Action Plan and is targeting an emission reduction of 25% and 80% by 2020 and 2050, respectively [
5]. No exception for the City of New York, where an 80% emission reduction by 2050 has been set as the goal [
6]. Buildings, as the foundation and major component of a city, are contributing the majority of the energy consumption and greenhouse gas emissions within the city [
3]. According to past studies, up to 75% of the energy consumption is contributed by urban buildings [
7,
8], and over 50% of the electricity consumption is consumed by residential and commercial buildings in a city [
3,
9]. Therefore, understanding urban building energy dynamics is essential for managing urban energy consumption, reducing greenhouse gas emissions, improving energy-use efficiency, developing urban sustainable development plans, and optimizing urban system design [
3,
10,
11,
12,
13,
14].
Urban Building Energy Modeling (UBEM) has been proved effective in simulating and understanding urban building energy consumption [
15,
16]. With UBEM, city governments can manage the existing urban building energy consumption and investigate future potential energy savings through testing new techniques and building codes [
17]. UBEM can be generally grouped into two branches: the top-down models and bottom-up models [
11,
18]. The top-down models analyze urban building energy consumption based on a group of buildings, and thus not able to analyze and explain the energy use of every single building [
19,
20]. Therefore, these models cannot provide a detailed energy-use analysis for a specific neighborhood. Moreover, most top-down models rely on historical data, which makes them difficult in testing the consequence of different energy retrofit strategies and technological advances. For instance, Hirst et al. [
21] simulated the annual residential energy consumption of the US based on an econometric model. Zhang [
22] examined the potential changes in regional energy use in China using the residents and corresponding energy consumption information, and also compared the results with other countries. Ozturk et al. [
23] and Canyurt et al. [
24] analyzed the relationship between energy use and demographic and economic factors using the genetic algorithms method in Turkey. While these studies have included major demographic, economic, and technological factors in energy-use modeling, new energy retrofit strategies and technological advances cannot be tested and verified as the models were only built based on past historical data on a large scale. In contrast, the bottom-up models focus on single buildings, where the energy use is thus analyzed for each building and, further, aggregated to the city, county, state, or national level. The bottom-up models are categorized into two types based on the modeling mechanisms: statistical models and physics-based models [
25]. The statistical model simulates the energy use of single buildings based on the collected historical energy-use data and social-economic data. Hirst et al. [
26] applied a regression model to analyze the impact of weather elements on household energy use based on utility data. Fung et al. [
27] used a regression model to explore the impact of demographics, weather, and other equipment characteristics on residential energy use in Canada. Parti and Parti [
28] used conditional demand analysis to analyze the relationship between household occupancy and electricity consumption in San Diego. Aydinalp et al. [
29,
30] proposed a national residential energy-use model based on neural networks. However, access to historical energy use and economic data may not be available for all cities. In contrast, the physics-based models estimate building energy use based on the physical characteristics of every single building. These models do not require any historical data as required by the top-down models and bottom-up statistical models, but require the knowledge of the building’s physical parameters, including the building’s shape, orientation, glazing, occupancy rates and schedule, envelope thermal properties, etc.
Several physics-based UBEM models have been proposed and applied in investigating urban building energy use in the past years. CitySim was developed by the Ecole Polytechnique Federale de Lausanne University in 2009, and it used a simplified thermal model to estimate urban building energy use at the district scale [
31]. While the accuracy of the proposed CitySim is limited as a simplified model used in energy-use estimations, it still can provide decision support for energy-use management and greenhouse gas emission reduction. Reinhart et al. [
32] developed an Urban Modeling Interface (UMI) in assessing building energy use performance, neighborhood walkability, and daylight potential under a Rhino-based environment. The sustainable design lab from the Massachusetts Institute of Technology proposed a UBEM model for the City of Boston in 2016. Specifically, the UBEM was developed based on GIS datasets and a custom-building archetype, and 83,541 buildings were generated using the CAD modelling and environment Rhinoceros 3D [
4]. The model has been calibrated and validated and is capable of estimating city-wide building energy use at the building level and hourly scale. City Building Energy Saver (CityBES), a web-based city-scale energy-use simulation and management platform, was developed by the Lawrence Berkeley National Laboratory. In particular, CityGML, an open data model for the storage and exchange of virtual 3D city models, was adopted by the CityBES for simulating building energy use and creating 3D building energy-use visualization [
15]. Li et al. [
12] simulated building energy use of the City of Des Moines, IA, with the newly developed CityBEUM model. Specifically, the energy-use mapping has been improved to the building level and hourly scale. Moreover, they also reported significant underestimation of electricity consumption in the summer and gas consumption in the winter, as well as overestimation of gas use in the spring when applying the Typical Meteorological Year (TMY) data in the model calibration and building energy-use simulation. Therefore, they emphasized the importance of applying actual weather data in urban building energy-use simulation.
Nowadays, the physics-based UBEMs have been widely applied in supporting urban energy-use management and greenhouse gas emission reduction throughout the world, such as in Boston [
4], Chicago [
33], Lisbon [
34], Kuwait [
35], Cambridge [
36], Des Moines [
12], Arriyadh [
37], etc. Numerous works have been conducted for New York City (NYC) as well. Specifically, Howard and Parshall [
19] proposed a model of energy consumption for NYC at a parcel level. Scofield [
38] analyzed the effect of certification on energy consumption in NYC. Ma and Cheng [
39] applied a geographic information system integrated data mining technology framework for estimating building energy-use intensity for NYC at an urban scale. Olivo and Hamidi [
40] analyzed the spatiotemporal variability of building energy use in NYC. However, most studies have been conducted at an urban scale or parcel scale, no study has been implemented at the building level yet. NYC, especially the Manhattan borough, is the most urbanized and populated area in the US, and NYC is facing a big challenge in reducing energy use and emissions. In April 2019, the Climate Mobilization Act, the most aggressive climate bill in the US, was passed by NYC to abide by the Paris climate change agreement, and NYC committed to reducing the carbon emission by 80% by 2050. Buildings contribute to almost 70% of the energy use and carbon emissions in NYC, and to reach the proposed carbon emission reduction target, several benchmarks have been prescribed in the new “80-by-50” law. Some buildings are required to reach the reduction goal earlier and different building types are subject to a specific target. For instance, buildings with total areas over 25,000 square feet need to reduce the emissions by 40% by 2030, and that is about 500,000 buildings in NYC. Therefore, an urban building energy-use model with a high spatial (building level) and temporal resolution (hourly scale) is essential for the city government and citizens in NYC for managing building energy use and implementing effective ways to reduce carbon emissions.
When implementing UBEM, weather data has been considered as one of the most important components, and most UBEM tools use the TMY weather data or the weather data from a local weather station in the model calibration and simulation. The importance of applying actual weather data in the model calibration and simulation has already been clarified by Li et al. [
12]. However, actual weather data from local weather stations may still not be enough for urban building energy use as actual weather data is commonly collected from the weather station from the airport, which is usually distributed in rural areas and far away from the downtown area in a city. Therefore, the impacts from the local microclimate have not been considered in the actual weather data. Several studies reported that the local microclimate, such as the urban heat island effect, could increase the temperature of the city’s downtown areas more than the surrounding rural areas [
41]. Therefore, it will increase the use of air conditioning, which, in turn, has a positive feedback on the urban heat island effect. Instead of applying actual weather data, localized weather data is needed in the urban building energy calibration and simulation process.
In this study, I proposed a work to quantify the building energy use of Manhattan in NYC with consideration of the local microclimate by integrating two popular modeling platforms, the Urban Weather Generator (UWG) and UBEM. The UWG was developed by Bueno and Norford [
42], and it can generate localized hourly weather data based on the referenced hourly weather data and local physical parameters. The UBEM has been widely used in many studies, and it is powerful in estimating building energy use at the building level and hourly scale. The paper is organized as follows: the study area, Manhattan in NYC, is introduced in
Section 2. the UWG and UBEM are described in
Section 3. The modeling results, including the spatial and temporal pattern of annual, monthly, and hourly building energy use, are reported in
Section 4. Conclusions are included in
Section 5.
5. Conclusions
In this study, the building energy-use dynamics of Manhattan, NYC, was modeled through integrating localized weather data and UBEM. Specifically, this study generated localized weather data based on the collected urban physical parameters and observed hourly weather data using UWG. A building energy-use model was established and calibrated for Manhattan, NYC, based on the collected RECS and CBEC reference data. Finally, building energy use was simulated and explored, to observe the spatial and temporal patterns of Manhattan, NYC.
The analysis results suggest several major conclusions. Firstly, the largest building electricity and gas uses are located in the center of Manhattan, which is mainly covered by commercial buildings with the largest building density and height. Secondly, similar seasonal electricity-use patterns and different seasonal gas-use patterns could be found in Manhattan. Specifically, the building electricity use is stable throughout all seasons. The largest gas consumption could be found in the winter due to high heating demand and low gas consumption in the summer as the gas is only used for water heating and cooking purpose. Thirdly, the summer energy use hourly profiles show only one peak for electricity use, mainly contributed by the high cooling demand. Winter energy use hourly profiles suggest two gas-use peaks. The first one is in the morning as people started working with high heating demand, and the second peak is associated with high heating demand from residential buildings when people finish their daily work and get back home.
While building energy use has been improved with localized weather data, there are still some other issues that need to be considered in the future, such as including the economic activity in the energy-use model. This study only modeled building energy use in the past. However, the understanding of future building energy use may be even more important as it could provide reference support for sustainable city planning. In 2014, the Intergovernmental Panel on Climate Change (IPCC) has released the fifth assessment report about future climate change, and the simulated future weather under different socio-development scenarios have been widely used in many studies already [
52,
53,
54]. Therefore, one possible future research direction could be estimating future building energy use with consideration of both the local microclimate and future climate change under different scenarios. In addition, the same building occupancy schedule was applied in the same building group in this study. However, buildings located in a different part of the city may have different occupancy schedules. Therefore, another future research direction could be improving building energy-use modeling with actual building occupancy schedules extracted from other data sources, such as socio-media data (e.g., Twitter. Facebook, etc.). Moreover, more accurate reference data is needed to improve the model performance. In this study, only the RECS and CBECS data from EIA in 2009 and 2012 were used as the reference data for model calibration. While the calibration performance is acceptable, the collected RECS and CBECS data are not very recent data; thus, the calibrated model may not be able to consider the current energy use conditions as impacted by the economy. The EIA is going to release new data in the future. The model could be updated later with new recent data to improve the performance. Moreover, the RECS and CBECS data are reported only at the regional level and the calibrated model may have a much better reflection of energy use at the regional level instead of the individual building level. In addition, the spatial information of the reference data from RECS and CBECS has been blocked to for privacy purposes. Only one energy-use model can be calibrated for one building type, and the spatial variation in energy use of each building type was ignored. When smart-metered utility data become available, the proposed model can be updated and improved for better modeling of building energy use at the individual building level, with consideration of the spatial variation in energy use within each building type.