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1. Application of Machine Learning for Predicting Building Energy Use at Different Temporal and Spatial Resolution under Climate Change in USA

   https://doi.org/10.3390/buildings10080139  

   Mohammadiziazi, Rezvan; Bilec, Melissa M. (August 2020, Buildings)

   Given the urgency of climate change, development of fast and reliable methods is essential to understand urban building energy use in the sector that accounts for 40% of total energy use in USA. Although machine learning (ML) methods may offer promise and are less difficult to develop, discrepancy in methods, results, and recommendations have emerged that requires attention. Existing research also shows inconsistencies related to integrating climate change models into energy modeling. To address these challenges, four models: random forest (RF), extreme gradient boosting (XGBoost), single regression tree, and multiple linear regression (MLR), were developed using the Commercial Building Energy Consumption Survey dataset to predict energy use intensity (EUI) under projected heating and cooling degree days by the Intergovernmental Panel on Climate Change (IPCC) across the USA during the 21st century. The RF model provided better performance and reduced the mean absolute error by 4%, 11%, and 12% compared to XGBoost, single regression tree, and MLR, respectively. Moreover, using the RF model for climate change analysis showed that office buildings’ EUI will increase between 8.9% to 63.1% compared to 2012 baseline for different geographic regions between 2030 and 2080. One region is projected to experience an EUI reduction of almost 1.5%. Finally, good data enhance the predicting ability of ML therefore, comprehensive regional building datasets are crucial to assess counteraction of building energy use in the face of climate change at finer spatial scale. 

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2. 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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3. Lifetime energy performance of residential buildings: A sensitivity analysis of energy modeling parameters

   Malekpour Koupaei, D. Geraudin (October 2020, Proceedings of the symposium on Simulation for Architecture and Urban Design)

   Chronis, A. (Ed.)

   Traditional building energy simulation tools often assess performance as a function of the unique climate, physical characteristics, and operational parameters that define specific buildings and communities, planned or existing. This paper presents the results of a sensitivity analysis on the input parameters(relating to both the building and climate) that affect the annual energy consumption loads of an existing residential neighborhood in the U.S. Midwest over the anticipated service life of its buildings using the Urban Modeling Interface (umi). Accordingly, first, the effect of multiple building construction characteristic packages and inclusion of outdoor vegetation, are investigated under typical meteorological climate conditions. Afterwards, since typical climate conditions may not adequately describe the potential extreme conditions that will be encountered over the entire service life of these buildings, alternative weather datasets were also utilized in the sensitivity analysis. The study’s findings suggest that cooling loads are expected to increase dramatically over the next five decades, both due to changes in the climate and the more wide-spread use of air-conditioning units. Since the results showed that trees can effectively reduce cooling loads by up to 7%, it is recommended that urban vegetation should be considered as an effective adaptation measure for facing the growing cooling demands. 

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4. Demand response through ventilation and latent load adjustment for commercial buildings in humid climate zones

   https://doi.org/10.1016/j.apenergy.2024.123940  

   Ma, Zhihao; Cui, Shuang; Chen, Jianli (November 2024, Applied Energy)

   Space cooling constitutes >10% of worldwide electricity consumption and is anticipated to rise swiftly due to intensified heatwaves under emerging climate change. The escalating electricity demand for cooling services will challenge already stressed power grids, especially during peak times of demand. To address this, the adoption of demand response to adjust building energy use on the end-user side becomes increasingly important to adapt future smart buildings with rapidly growing renewable energy sources. However, existing demand response strategies predominantly explore sensible cooling energy as flexible building load while neglecting latent cooling energy, which constitutes significant portions of total energy use of buildings in humid climates. Hence, this paper aims to evaluate the demand response potential by adjusting latent cooling energy through ventilation control for typical medium commercial office buildings in four representative cities across different humid climate zones, i.e., Miami, Huston, Atlanta, and New York in the United States (US). As the first step, the sensible heat ratio, defined as sensible cooling load to total building load (involving both sensible and latent load), in different humid climates are calculated. Subsequently, the strategy to adjust building latent load through ventilation control (LLVC) is explored and implemented for demand response considering the balance of energy shifting, indoor air quality, and energy cost. Results reveal that adjusting building ventilation is capable of achieving 30%–40% Heating, Ventilation, and Air-conditioning (HVAC) cooling demand flexibility during HVAC operation while among this, the latent cooling energy contributes 56% ~ 66.4% to the overall demand flexibility. This work provides a feasible way to improve electricity grid flexibility in humid climates, emphasizing the significant role of adjusting latent cooling energy in building demand response. 

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5. The Impact of Urban Heat Island on Calibrated Building Energy Model Predictions

   Jahani, Elham; Vanage, Soham; Cetin, Kristen (January 2020, ASCE Construction Research Congress)

   Among various elements of urban infrastructure, there is significant opportunity to improve existing buildings’ sustainability, considering that approximately 40% of the total primary energy consumption and 72% of electricity consumption in United States is consumed by the building sector. Many different efforts focus on reducing the energy consumption of residential buildings. Data-validated building energy modeling methods serve the role of supporting this effort, 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 energy model results, one of which is the weather input data. Measured weather data inputs located at each building can help address this concern, however, weather station data collection for each building is also costly and typically not feasible. Some weather station data is already collected, however, these are generally located at airports rather than near buildings, and thus do not capture local, spatially-varying weather conditions which are documented to occur, particularly in urban areas. In this study we address the impact of spatial temperature differences on residential building energy use. An energy model was developed in EnergyPlus for a residential building located in Mueller neighborhood of Austin, TX, and was validated using actual hourly measured electricity consumption. Using the validated model, the impact of measured spatial temperature differences on building energy consumption were investigated using multiple weather stations located throughout the urban area with different urban fractions. The results indicate that energy consumption of a residential building in a city with a 10% higher urban fraction would increase by approximately 10%. This variation in energy consumption is likely due to the impact of UHI effects occurring in urban areas with high densities. 

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