This Fact Sheet provides an overview of the Better Buildings Workforce Guidelines project. The Department of Energy (DOE) and the National Institute of Building Sciences (NIBS) are working with industry stakeholders to develop voluntary national guidelines that will improve the quality and consistency of commercial building workforce training and certification programs for five key energy-related jobs.
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The lack of empirical data on the energy performance of buildings is a key barrier to accelerating the energy efficiency retrofit market. The DOE’s Buildings Performance Database (BPD) helps address this gap by allowing users to perform exploratory analyses on an anonymous dataset of hundreds of thousands of commercial and residential buildings. These analyses enable market actors to assess energy efficiency opportunities, forecast project performance, and quantify performance risk using empirical building data. In this paper, we describe the process of collecting and preparing data for the database, and present a peer-group analysis tool that allows users to analyze building performance for narrowly defined subsets of the database, or peer groups. We use this tool to explore a case study of a multifamily portfolio owner comparing his buildings’ performance to the peer group of multifamily buildings in the local metro area. We also present a performance comparison tool that uses statistical methods to estimate the expected change in energy performance due to changes in building-component technologies. We demonstrate a low-effort retrofit analysis, providing a probabilistic estimate of energy savings for a sample building retrofit. The key advantages of this approach compared to conventional engineering models are that it provides probabilistic risk analysis based on actual
measured data and can significantly reduce transaction costs for predicting savings across a portfolio.
While the availability of “big data” about building energy performance is increasing in response to market demands and public policies, the lack of standard data formats is a significant ongoing barrier to its full utilization. To overcome this barrier, the U.S. Department of Energy (DOE) and Lawrence Berkeley National Laboratory (LBNL) developed the Building Energy Data Exchange Specification (BEDES).
BEDES is designed to enable the exchange, comparison, and combination of empirical information by providing common terms and definitions for data about commercial and residential building’s physical and operational characteristics, energy use, and efficiency measures.
This paper describes the BEDES development process, scope, structure, and plans for implementation and ongoing updates.
One of the nation’s largest schools serving over 60,000 students, the University of Minnesota (U of M) is upgrading the lighting at all 18 parking ramps and garages on its Minneapolis campus. In the Northrop Auditorium Garage, a small 24,000 square foot facility with 75 parking spots, U of M replaced low-wattage high-pressure sodium fixtures with high efficiency, lower- wattage LED fixtures with lighting controls. This Lighting Energy Efficiency in Parking (LEEP) Campaign Award winning project achieved 90% energy savings by upgrading to LEDs with lighting controls.
NorthBay VacaValley Hospital completed lighting retrofits to their 150,000 square foot parking lot and its 225 parking spaces. They did so with help from The California Lighting Technology Center (CLTC) at the University of California, Davis. The project has achieved 65% savings and received a 2014 Lighting Energy Efficiency in Parking (LEEP) Campaign’s award for best use of lighting controls. In addition, the retrofits improved lighting maintenance operations and end-user satisfaction.
The lighting retrofit included replacing roughly 50 induction luminaires with new LED fixtures with embedded lighting controls.
The new LED fixtures were coupled with various kinds of lighting control systems, including a radio frequency (RF) connectivity control system that was installed in dedicated zones with passive- infrared (PIR) and long-range microwave sensors to achieve energy savings. An “ultra-smart” lighting control network was also put in place, giving facility managers the ability to adjust lighting schedules, light levels and time-out settings, monitor the system’s energy use, and receive automated alerts when luminaires require maintenance.