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1. Performance Modeling and Analysis of a Thermoelectric Building Envelope for Space Heating.

   Liu, X. ; Qu, M. ; Yazawa, K. ; Kohanoff, J. ; Chudzinski, P. ; Stella, L. ; Norton, B. ; Holmes, N. ; Jani, R. ; Yin, H. ( July 2022 , 2022 (7th) International High Performance Buildings Conference)

   To provide energy-efficient space heating and cooling, a thermoelectric building envelope (TBE) embeds thermoelectric devices in building walls. The thermoelectric device in the building envelope can provide active heating and cooling without requiring refrigerant use and energy transport among subsystems. Thus, the TBE system is energy and environmentally friendly. A few studies experimentally investigated the TBE under limited operating conditions, and only simplified models for the commercial thermoelectric module (TEM) were developed to quantify its performance. A holistic approach to optimum system performance is needed for the optimal system design and operation. The study developed a holistic TBE-building system model in Modelica for system simulation and performance analysis. A theoretical model for a single TEM was first established based on energy conversion and thermoelectric principles. Subsequently, a TBE prototype model combining the TEM model was constructed. The prototype model employing a feedback controller was used in a whole building system simulation for a residential house. The system model computed the overall building energy efficiency and dynamic indoor conditions under varying operating conditions. Simulation results indicate the studied TBE system can meet a heating demand to maintain the desired room temperature at 20 °C when the lowest outdoor temperature is atmore »-26.3 °C, with a seasonal heating COP near 1.1, demonstrating a better heating performance than electric heaters. It suggests a potential energy-efficient alternative to the traditional natural gas furnaces and electric heaters for space heating.« less
2. 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 almostmore »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.« less
3. 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 validatedmore »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.« less
4. Impact Assessment in the Process of Propagating Climate Change Uncertainties into Building Energy Use

   https://doi.org/10.3390/en14020367  

   Yassaghi, Hamed ; Hoque, Simi ( January 2021 , Energies)

   Buildings are subject to significant stresses due to climate change and design strategies for climate resilient buildings are rife with uncertainties which could make interpreting energy use distributions difficult and questionable. This study intends to enhance a robust and credible estimate of the uncertainties and interpretations of building energy performance under climate change. A four-step climate uncertainty propagation approach which propagates downscaled future weather file uncertainties into building energy use is examined. The four-step approach integrates dynamic building simulation, fitting a distribution to average annual weather variables, regression model (between average annual weather variables and energy use) and random sampling. The impact of fitting different distributions to the weather variable (such as Normal, Beta, Weibull, etc.) and regression models (Multiple Linear and Principal Component Regression) of the uncertainty propagation method on cooling and heating energy use distribution for a sample reference office building is evaluated. Results show selecting a full principal component regression model following a best-fit distribution for each principal component of the weather variables can reduce the variation of the output energy distribution compared to simulated data. The results offer a way of understanding compound building energy use distributions and parsing the uncertain nature of climate projections.
5. What drives our behaviors in buildings? A review on occupant interactions with building systems from the lens of behavioral theories

   https://doi.org/10.1016/j.buildenv.2020.106928  

   Arsalan Heydarian, Claire McIlvennie ( July 2020 , Building and environment)

   Occupant behavior has a significant impact on building systems’ operations and efficiency. As a result, several innovative approaches have been introduced to quantify the dynamics of occupants within indoor environments, such as interactions with different building systems and the impact of various feedback and interventions to reduce the building energy consumption. To achieve this, researchers have highlighted the importance of reducing energy consumption without impacting occupant comfort. As a result, there is an increasing body of research evaluating how different theories of behavior across a variety of disciplines can explain occupant interactions with building systems. Future progress in this area calls for an in-depth understanding of behavioral theories in explaining occupant interactions with different building systems. In this paper, we have used a structured literature review approach to investigate how different psychological, sociological, and economic theories have been applied to explain occupant interactions with heating and cooling (HVAC systems), opening windows and ventilation, lighting and shading, electronic appliances, domestic hot water, as well as energy conservation behaviors. Throughout the paper, we identify the most common theories and methodologies applied within the existing research, general findings related to how occupants interact with different building systems, as well as a number ofmore »identified gaps within the literature. Finally, we provide a discussion on directions for future research studies in this area under each building system.« less