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1. Energy and Demand Saving Potential due to Integrated HVAC, Lighting, and Shading Controls in Small Office Building

   https://doi.org/10.1061/9780784483961.047  

   Vanage, Soham ; Dong, Hao ; Cetin, Kristen ( January 2022 , ASCE Construction Research Congress 2022)

   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. 

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2. Co-Simulation and Validation of Automated Shading Devices using EnergyPlus and Radiance to optimize Building Energy Use

   Vanage, Soham ; Kunwar, Niraj ; Malekpour, Diba ; Cetin, Kristen ( January 2022 , ASHRAE winter conference papers)

   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. 

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3. Energy Use Sensitivity Analysis of Sensor Placement in Small Office Buildings with Dynamic Shading and Lighting

   Dong, Hao ; Vanage, Soham ; Cetin, Kristen ( January 2022 , ASHRAE Annual Conference 2022)

   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. 

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4. Exploring Deep Reinforcement Learning for Holistic Smart Building Control

   https://doi.org/10.1145/3656043  

   Ding, Xianzhong ; Cerpa, Alberto ; Du, Wan ( May 2024 , ACM Transactions on Sensor Networks)

   In recent years, the focus has been on enhancing user comfort in commercial buildings while cutting energy costs. Efforts have mainly centered on improving HVAC systems, the central control system. However, it’s evident that HVAC alone can’t ensure occupant comfort. Lighting, blinds, and windows, often overlooked, also impact energy use and comfort. This paper introduces a holistic approach to managing the delicate balance between energy efficiency and occupant comfort in commercial buildings. We presentOCTOPUS, a system employing a deep reinforcement learning (DRL) framework using data-driven techniques to optimize control sequences for all building subsystems, including HVAC, lighting, blinds, and windows.OCTOPUS’s DRL architecture features a unique reward function facilitating the exploration of tradeoffs between energy usage and user comfort, effectively addressing the high-dimensional control problem resulting from interactions among these four building subsystems. To meet data training requirements, we emphasize the importance of calibrated simulations that closely replicate target-building operational conditions. We trainOCTOPUSusing 10-year weather data and a calibrated building model in the EnergyPlus simulator. Extensive simulations demonstrate thatOCTOPUSachieves substantial energy savings, outperforming state-of-the-art rule-based and DRL-based methods by 14.26% and 8.1%, respectively, in a LEED Gold Certified building while maintaining desired human comfort levels.

    

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5. Recurrent Mobility: Urban Conduits for Diffusion of Energy Efficiency

   https://doi.org/10.1038/s41598-019-56372-4  

   Mohammadi, Neda ; Taylor, John E. ( December 2019 , Scientific Reports)

   Recent advances in energy technologies, policies, and practices have accelerated the global rate of improvements in energy efficiency, bringing the energy targets identified in the 2030 United Nations (UN) Sustainable Development Agenda within reach. However, Target 7.3 requires this rate to double by 2030, demanding a more substantial response to energy interventions. At present, energy interventions are failing to reach optimal levels of adoption in buildings, which are the largest urban energy consumers. This is due to a combination of direct and indirect effects generally referred to as the energy efficiency gap. Here, we compare over 18.8 million positional records of individuals against Greater London’s buildings energy consumption records over the course of one year. We demonstrate that indirect (i.e., spillover) effects, arising fromrecurrent mobility, govern the diffusion of urban buildings’ energy efficiency, far outpacing direct effects. This has been understood as a consequence of underlying spatiotemporal dependencies at the intersection of energy use and social interactions. We add to this the critical role of recurrent mobility (i.e., the mobility of those urban populations who repeatedly visit certain locations, such as home and work) as a diffusion conduit. These findings suggest that in order to improve the current levels of adoption, interventions must target times and locations that function as dense hubs of energy consumption and social interactions. Recurrent mobility thus provides a viable complement to existing targeted intervention approaches aimed at improving energy efficiency, supporting efforts to meet the UN’s 2030 energy targets.

    

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