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Vrabel, Katie

Publications and source records attributed to Vrabel, Katie.

Long and Winding Road to Higher Efficiency-The RTU Story: Preprint

Rooftop units (RTUs) and other packaged heating, ventilating, and air-conditioning (HVAC) equipment consume more than four quads of energy annually while conditioning more than 50% of the commercial building floor area in the United States. Historically, these systems have low operating efficiencies and receive infrequent maintenance. In addition, the market has a low first cost, run-to-failure, like-for-like replacement mentality and has been slow to adopt change. This paper explores the broad market transformation that has resulted in higher efficiencies and tremendous energy savings. Although there are many factors in this market transformation, this paper highlights: research behind advancements in components and controls; adoption of an operational efficiency metric; raising the bar for high efficiency with the RTU Challenge; market barriers overcome through the Advanced RTU Campaign; upstream and midstream incentive programs and HVAC distribution networks; alignment of the market efficiency drivers of ASHRAE Standard 90.1, federal minimum efficiency standards, the ENERGY STAR® program, and the Consortium for Energy Efficiency's (CEE's) efficiency tiers. Measures of the market transformation include more than 50% increase in RTU efficiencies, more than 1 billion kWh saved and 160,000 RTUs upgraded with high-efficiency measures by the Advanced RTU Campaign partners, large increases of high-efficiency RTUs purchased through incentive programs, and the largest energy savings from any federal minimum standards action. Although these results are impressive, the market transformation is just beginning, and there remain exciting opportunities for improvements in equipment efficiencies and improved operating performance with advanced controls and fault detection and diagnostics.

30 DIRECT ENERGY CONVERSION↗

Emerging Technologies for Improved Plug Load Management Systems: Learning Behavior Algorithms and Automatic and Dynamic Load Detection

Plug loads are responsible for a significant portion of the energy consumed in commercial buildings, yet their distributed and ever-changing nature makes them one of the most challenging building end uses to manage. Plug load management systems exist today that utilize smart plugs to meter and control devices at the outlet level, however, their uptake has been relatively slow in part due to the significant labor required for installation and maintenance. Learning behavior algorithms and automatic and dynamic load detection have been identified as two technology areas that could accelerate the adoption of plug load management systems by reducing these labor demands and providing additional energy efficiency and non-energy benefits. Learning behavior algorithms learn occupant behavior and adjust plug load management systems accordingly, allowing for the automatic creation of optimized control schedules. Automatic and dynamic load detection allows a plug load management system to identify devices as they are plugged in to a building and keeps the system up to date as devices are moved throughout a building. In this paper, we present our findings with respect to the current state of these two technologies based on a review of existing research and patents, as well as a series of interviews with companies working in the plug load space. We have found that, as of now, no commercialized solutions exist for these plug load technologies and that more work is needed to bring them to market. In addition, we summarize our findings related to the technology challenges, market barriers, drivers, and opportunities for these technologies moving forward.

30 DIRECT ENERGY CONVERSION↗