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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. 

As the energy consumption from residential and commercial buildings makes up approximately three-quarters of the U.S. electricity loads, analyzing building energy consumption behavior becomes essential for effective power grid operation. An accurate physics-based building energy simulator that is built on first principles can predict an individual building’s energy response, such as energy consumption and indoor environmental conditions under different weather and operational control scenarios. In the building energy simulator, several parameters that specify building characteristics need to be set a priori. Among those parameters, some parameters are season-dependent, whereas other parameters should be globally employed throughout a year. Existing studies in parameter calibration ignore such heterogeneity, which causes suboptimal calibration results. This study presents a new calibration approach that considers the seasonal dependency.