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1. Sensitivity of Building Energy Model Predictions to Spatial Variations in Climate under Different Levels of Urbanization

   Jahani, Elham; Vanage, Soham; Cetin, Kristen; Gallus, William; Jahn, David (January 2020, ASHRAE Annual Virtual Conference 2020)

   null (Ed.)

   In the United States, approximately 40% of the primary energy use and 72% of the electricity use belong to the building sector. This shows the significance of studying the potential for reducing the building energy consumption and buildings’ sustainability for ensuring a sustainable development. Therefore, many different efforts focus on reducing the energy consumption of residential buildings. Data-validated building energy modeling methods are among the studies for such an effort, particularly, by enabling the identification of the potential savings associated with different potential retrofit strategies. However, there are many uncertainties that can impact the accuracy of such energy model results, one of which is the weather input data. In this study, to investigate the impact of spatial temperature variation on building energy consumption, six weather stations in an urban area with various urban density were selected. A validated energy model was developed using energy audit data and high-frequency electricity consumption of a residential building in Austin, TX. The energy consumption of the modeled building was compared using the selected six weather datasets. The results show that energy use of a building in an urban area can be impacted by up to 12% due to differences in urban density. This indicates the importance of weather data in predicting energy consumption of the building. The methodology and results of this study can be used by planners and decision makers to reduce uncertainties in estimating the building energy use in urban scale. 

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2. Urban Weather Predictions Compared to a Dense Network of Ground-Based Weather Station Data for Assessment of Urban Building Energy Consumption

   Jahani, E; Vanage, S.; Jahn, D.; Gallus, W.; Cetin, K.S. (June 2019, ASHRAE Annual Conference papers)

   Building energy consumption is highly influenced by weather conditions, thus having appropriate weather data is important for improving the accuracy of building energy models. Typically local weather station data from the nearest airport or military base is used for weather data input. However this is generally known to differ from the actual weather conditions experienced by an urban building, particularly considering most weather stations are located far from urban areas. The use of the Weather Research and Forecasting Model (WRF) coupled with an Urban Canopy Model (UCM) provides a means to be able to predict more localized variations in weather conditions. However, one of the main challenges associated with the assessment of the use of this model is the lack of availability of ground based weather station data with which to compare its results. This has generally limited the ability to assess the level of agreement between WRF-UCM weather predictions and measured weather data in urban locations. In this study, a network of 40 ground based weather stations located in Austin, TX are compared to WRF/UCM-predicted weather data, to assess similarities and differences between model-predicted results and actual data. Given that the WRF-UCM method also takes into account many input parameters and assumptions, including the urban fraction which can be measured at different scales, this work also considers the relative impact of the granularity of the urban fraction data on WRF-UCM predicted weather. As a case study, a building energy model of a typical residential building is then developed and used to assess the differences in predicted building energy use and demands between the WRF-UCM weather and measured weather conditions during an extreme heatwave event in Austin, TX 

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3. 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)

   null (Ed.)

   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 Forecasting 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. 

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4. 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 Forecasting 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. 

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5. Comparative Analysis of Urban Heat Island Effects on Building Energy Consumption in the U.S. Midwest: A combined workflow using Urban Weather Generator and Future Typical Meteorological Year Climate Scenarios

   HASHEMI, FARZAD; SALAHI, NEGAR; GHIASI, SEDIGHEH; PASSE, ULRIKE (October 2024, Wrocław University of Science and Technology Publishing House)

   Widera, Barbara; Rudnicka-Bogusz, Marta; Onyszkiewicz, Jakub; Woźniczka, Agata (Ed.)

   Urban areas often experience higher air temperatures than their surrounding rural counterparts, a phenomenon known as the urban heat island (UHI) effect. This significant human-induced alteration of urban microclimates has notable consequences, especially on urban energy consumption and resulting economic implications. This study presents an in-depth analysis of the UHI effect on urban building energy consumption in a US Midwest neighbourhood. Utilizing a three-phase methodology, the research first simulated UHI intensities with current and future Typical Meteorological Year (TMY) data, integrated with the Local Climate Zone (LCZ) classification system and the Urban Weather Generator (UWG) model. The second phase employed the urban modelling interface (umi) for building energy simulation, capturing the UHI impact on both residential and commercial buildings. The third phase demonstrates that UHI effects lead to reduced heating demand but increased cooling requirements in the future, with residential areas being more affected. The study's findings reveal critical challenges for urban planners and policymakers, emphasizing the need for sustainable designs to address fluctuating heating and cooling demands in changing climates. 

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