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Buildings represent the largest contributor to energy consumption in electric grids of the United States, making them a significant focal point for energy improvements and sustainability efforts. The broad participation of residential buildings in demand side management (DSM) can support decarbonization goals and the use of power generated by clean energy sources. The purpose of this study is to assess awareness and potential factors that may influence college students' willingness for load flexibility to support DSM participation. An online survey was conducted among students majoring in civil, environmental, and applied engineering in two distinct classes. Preliminary findings suggest that enhanced awareness of the DSM strategies reduce levels of concern with participation in demand side management programs. The factors that appear to drive willingness to participate in DSM for this specific population are related to the potential reduction of electricity costs, helping the environment, and overall energy savings. 

Buildings are responsible for the largest portion of energy consumption on U.S. electric grids. The wide participation of buildings in demand side management (DSM) through modulating or shifting electricity end uses, particularly in homes, can support decarbonization goals and increase reliability of electric power supply. The awareness and willingness of households to adjust internal loads, housing occupancy, and household energy consumption patterns all play an important role to support the potential for DSM. A particularly challenging type of housing to reach in DSM is rental housing. Historically this type of housing has been plagued by split incentives that limit the motivation of home owners (landlords) to improve the energy performance of these buildings since they often do not pay the utility bills. DSM presents an opportunity to support reducing the utility bills of renters through controls adjustments rather than requiring the landlord to invest in energy efficient technologies. This study aims to identify household occupancy schedules and potential factors that may influence willingness to participate in DSM among renters, in particular college students. A survey-based method was conducted among 55 college students majoring in civil, environmental, and applied engineering and showed that, while the targeted population has low awareness about DSM strategies, they would be willing to participate in a DSM in the future. The factors that appear to drive willingness to participate in DSM for this population were related to the potential reduction of electricity costs and energy savings. 

Parameter calibration aims to estimate unobservable parameters used in a computer model by using physical process responses and computer model outputs. In the literature, existing studies calibrate all parameters simultaneously using an entire data set. However, in certain applications, some parameters are associated with only a subset of data. For example, in the building energy simulation, cooling (heating) season parameters should be calibrated using data collected during the cooling (heating) season only. This study provides a new multiblock calibration approach that considers such heterogeneity. Unlike existing studies that build emulators for the computer model response, such as the widely used Bayesian calibration approach, we consider multiple loss functions to be minimized, each for a block of parameters that use the corresponding data set, and estimate the parameters using a nonlinear optimization technique. We present the convergence properties under certain conditions and quantify the parameter estimation uncertainties. The superiority of our approach is demonstrated through numerical studies and a real-world building energy simulation case study. History: Bianca Maria Colosimo served as the senior editor for this article. Funding: This work was partially supported by the National Science Foundation [Grants CMMI-1662553, CMMI-2226348, and CBET-1804321]. Data Ethics & Reproducibility Note: The code capsule is available on Code Ocean at https://codeocean.com/capsule/8623151/tree/v1 and in the e-Companion to this article (available at https://doi.org/10.1287/ijds.2023.0029 ). 

This dataset contains de-identified data collected during the energy assessments conducted in Unalakleet, Alaska in from May to August 2021. It does not contain identifiable information of participants. The datasets are divided by type of housing characteristics analyzed. contains information on personal housing challenges, community housing concerns, preferences for future housing design and construction and climate change impacts. This dataset provides Alaska Native community perspectives regarding housing challenges and solutions using a community-based participatory research approach. 

With commercial and residential buildings accounting for approximately 40% of the energy and 70% of the electricity consumption in the United States, there are substantial opportunities to improve energy efficiency in these buildings. Similarly, buildings also account for the large majority of electricity demand, particularly during peak use hours. As the electric grid becomes increasingly supported by renewable energy, buildings are ideal for supporting demand-side management, allowing for the electricity demand to meet the variable levels of electricity supply. Integrated controls of various building energy system components, including HVAC (Heating Ventilation and Air Conditioning), lighting, and shading devices, combined with advanced sensor and control technologies, can help to optimize system operations. This research aims to study the impact of integrated HVAC, lighting, and shading device controls, to estimate energy and demand saving in typical small office buildings in the U.S. This is achieved through a multi-step modeling process, including daylight simulation using Radiance to evaluate available daylight for each zone, then EnergyPlus to develop and implement various controls and estimate energy and demand savings using the Radiance results as input. The result of this work provides insights for a variety of stakeholders in the building, utility and grid operator industries and quantifies the potential benefit of integrated systems. 

The main energy end uses in commercial buildings include cooling, heating, and lighting. These energy consuming systems, however, can be substantially impacted by environmental parameters and sensor inputs when a building is being dynamically controlled. This study aims to conduct a sensitivity analysis on the energy consumption of a small commercial office building with an integrated control system, including automated shade devices and dimmable lighting. Previous studies have focused on sensitivity of automated shades energy impacts, based on glare level, solar irradiation, available daylighting and solar penetration; others have assessed the sensitivity of dimmable lighting on energy use. The focus of this study is to assess the impact of adjusting illuminance sensor location, and sensor rotation (towards or away from the exterior windows), for small office buildings with integrated shading and lighting controls in different ASHRAE climate zones. 

Buildings in the U.S. are responsible for approximately 40% of energy and 70% of the electricity consumption. To address rising greenhouse gas emissions and climate changes, various studies have explored strategies to reduce energy consumption in buildings. One opportunity to improve the building envelope performance is through improvements to fenestrations, particularly complex multi-layer fenestration systems for exterior windows. Windows are the least thermally efficient of all components in a typical building envelope. Windows also permit solar radiation into a building, which significantly increases the building energy consumption during the summer season. Meanwhile, windows are necessary to provide occupants with natural light, a view to the outside, and to support productivity. Thus, there is a need to strike a balance between energy savings, and the thermal and visual comfort impacted by windows. Traditionally, shading devices are one method used to adjust the amount of heat and light entering an interior space. However, such shading devices are typically operated manually by occupants, and are seldom used effectively over time. Currently the building energy simulation program EnergyPlus, has limited capabilities to model shading devices, and more limited abilities to model dynamic fenestrations. In this study, thus, we propose to model and validate several types of automated multi-layer fenestration elements, using co-simulation of EnergyPlus and Radiance using laboratory-collected data. EnergyPlus was used to model energy consumption and thermal comfort while Radiance was used to model lighting levels. BCVTB was used to interface between EnergyPlus and Radiance to facilitate co-simulation. To validate the models, experimental data was collected from 5 illuminance sensors in an exterior office space located in a test facility in Ankeny, IA. This model methodology can be used to improve the flexibility and modeling capabilities of dynamic fenestration elements for building energy performance evaluation methods.