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1. Mitigation Options to Reduce Peak Air Temperature and Air-Conditioning Demand in the Context of a Warming Climate for a Tropical Coastal City

   https://doi.org/10.1115/1.4051160  

   Pokhrel, Rabindra ; González-Cruz, Jorge E. ( May 2021 , ASME Journal of Engineering for Sustainable Buildings and Cities)

   Abstract Air conditioning (AC) demand has recently grown to about 10% of total electricity globally, and the International Energy Agency (IEA) predicts that the cooling requirement for buildings globally increases by three-fold by 2050 without additional policy interventions. The impacts of these increases for energy demand for human comfort are more pronounced in tropical coastal areas due to the high temperatures and humidity and their limited energy resources. One of those regions is the Caribbean, where building energy demands often exceed 50% of the total electricity, and this demand is projected to increase due to a warming climate. The interconnectionmore »between the built environment and the local environment introduces the challenge to find new approaches to explore future energy demand changes and the role of mitigation measures to curb the increasing demands for vulnerable tropical coastal cities due to climate change. This study presents mid-of-century and end-of-century cooling demand projections along with demand alleviation measures for the San Juan Metropolitan Area in the Caribbean Island of Puerto Rico using a high-resolution configuration of the Weather Research and Forecasting (WRF) model coupled with Building Energy Model (BEM) forced by bias-corrected Community Earth Systems Model (CESM1) global simulations. The World Urban Database Access Portal Tool (WUDAPT) Land Class Zones (LCZs) bridge the gap required by BEM for their morphology and urban parameters. MODIS land covers land use is depicted for all-natural classes. The baseline historical period of 2008–2012 is compared with climate and energy projections in addition to energy mitigation options. Energy mitigation options explored include the integration of solar power in buildings, the use of white roofs, and high-efficiency heating, ventilation, and air conditioning (HVAC) systems. The impact of climate change is simulated to increase minimum temperatures at the same rate as maximum temperatures. However, the maximum temperatures are projected to rise by 1–1.5 °C and 2 °C for mid- and end-of-century, respectively, increasing peak AC demand by 12.5% and 25%, correspondingly. However, the explored mitigation options surpass both increases in temperature and AC demand. The AC demand reduction potential with energy mitigation options for 2050 and 2100 decreases the need by 13% and 1.5% with the historical periods. Overall, the demand reduction potential varies with LCZs showing a high reduction potential for sparsely built (32%), and low for compact low rise (21%) for the mid-of-century period compared with the same period without mitigation options.« less
2. Adapting to Extreme Heat: Social, Atmospheric, and Infrastructure Impacts of Air-Conditioning in Megacities—The Case of New York City

   https://doi.org/10.1115/1.4048175  

   Gamarro, Harold ; Ortiz, Luis ; González, Jorge E. ( August 2020 , ASME Journal of Engineering for Sustainable Buildings and Cities)

   Abstract Extreme heat events are becoming more frequent and intense. In cities, the urban heat island (UHI) can often intensify extreme heat exposure, presenting a public health challenge across vulnerable populations without access to adaptive measures. Here, we explore the impacts of increasing residential air-conditioning (AC) adoption as one such adaptive measure to extreme heat, with New York City (NYC) as a case study. This study uses AC adoption data from NYC Housing and Vacancy Surveys to study impacts to indoor heat exposure, energy demand, and UHI. The Weather Research and Forecasting (WRF) model, coupled with a multilayer building environmentmore »parameterization and building energy model (BEP–BEM), is used to perform this analysis. The BEP–BEM schemes are modified to account for partial AC use and used to analyze current and full AC adoption scenarios. A city-scale case study is performed over the summer months of June–August 2018, which includes three different extreme heat events. Simulation results show good agreement with surface weather stations. We show that increasing AC systems to 100% usage across NYC results in a peak energy demand increase of 20%, while increasing UHI on average by 0.42 °C. Results highlight potential trade-offs in extreme heat adaptation strategies for cities, which may be necessary in the context of increasing extreme heat events.« less
3. CITY-SCALE ENERGY MODELING TO ASSESS IMPACTS OF EXTREME HEAT ON ELECTRICITY CONSUMPTION AND PRODUCTION USING WRF-UCM MODELING WITH BIAS CORRECTION

   Jahani, E ; Vanage, S. ; Jahn, D. ; Gallus, W. ; Cetin, K.S. ( June 2019 , Canadian Society of Civil Engineers Annual Conference)

   The energy consumption of buildings at the city scale is highly influenced by the weather conditions where the buildings are located. Thus, having appropriate weather data is important for improving the accuracy of prediction of city-level energy consumption and demand. Typically, local weather station data from the nearest airport or military base is used as input into building energy models. However, the weather data at these locations often differs from the local weather conditions experienced by an urban building, particularly considering most ground-based weather stations are located far from many urban areas. The use of the Weather Research and Forecastingmore »Model (WRF) coupled with an Urban Canopy Model (UCM) provides means to predict more localized variations in weather conditions. However, despite advances made in climate modeling, systematic differences in ground-based observations and model results are observed in these simulations. In this study, a comparison between WRF-UCM model results and data from 40 ground-based weather station in Austin, TX is conducted to assess existing systematic differences. Model validations was conducted through an iterative process in which input parameters were adjusted to obtain to best possible fit to the measured data. To account for the remaining systemic error, a statistical approach with spatial and temporal bias correction is implemented. This method improves the quality of the WRF-UCM model results by identifying the statistic properties of the systematic error and applying several bias correction techniques.« less
4. CITY-SCALE ENERGY MODELING TO ASSESS IMPACTS OF EXTREME HEAT ON ELECTRICITY CONSUMPTION AND PRODUCTION USING WRF-UCM MODELING WITH BIAS CORRECTION

   Jahani, Elham ; Vanage, Soham ; Cetin, Kristen ; Gallus, William ( January 2019 , CSCE Annual Conference)

   The energy consumption of buildings at the city scale is highly influenced by the weather conditions where the buildings are located. Thus, having appropriate weather data is important for improving the accuracy of prediction of city-level energy consumption and demand. Typically, local weather station data from the nearest airport or military base is used as input into building energy models. However, the weather data at these locations often differs from the local weather conditions experienced by an urban building, particularly considering most ground-based weather stations are located far from many urban areas. The use of the Weather Research and Forecastingmore »Model (WRF) coupled with an Urban Canopy Model (UCM) provides means to predict more localized variations in weather conditions. However, despite advances made in climate modeling, systematic differences in ground-based observations and model results are observed in these simulations. In this study, a comparison between WRF-UCM model results and data from 40 ground-based weather station in Austin, TX is conducted to assess existing systematic differences. Model validations was conducted through an iterative process in which input parameters were adjusted to obtain to best possible fit to the measured data. To account for the remaining systemic error, a statistical approach with spatial and temporal bias correction is implemented. This method improves the quality of the WRF-UCM model results by identifying the statistic properties of the systematic error and applying several bias correction techniques.« less
5. Passive survivability of buildings under changing urban climates across eight US cities

   https://doi.org/10.1088/1748-9326/ab28ba  

   Baniassadi, Amir ; Sailor, David J. ; Krayenhoff, E. Scott ; Broadbent, Ashley M. ; Georgescu, Matei ( July 2019 , Environmental Research Letters)

   In the US, more than 80% of fatal cases of heat exposure are reported in urban areas. Notably, indoor exposure is implicated in nearly half of such cases, and lack of functioning air conditioning (AC) is the predominant cause of overheating. For residents with limited capacity to purchase, maintain, and operate an AC system, or during summertime power outages, the ability of buildings to maintain safe thermal conditions without mechanical cooling is the primary protective factor against heat. In this paper, we use whole-building energy simulations to compare indoor air temperature inside archetypical single-family residential buildings without AC atmore »the start and middle of the century in eight US cities. We ran the models using hourly output from 10 year regional climate simulations that explicitly include heating from mid-century projections of urban development and climate change under a ‘business-as-usual’ emissions scenario. Moreover, to identify the impacts from evolving construction practices, we compare different versions of building energy standards. Our analysis shows that summertime overheat time may increase by up to 25% by the middle of century. Moreover, we find that, while newer building energy codes reduce thermal comfort under moderate outdoor weather, they perform better under extreme heat.

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