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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A guide to Financing Tools for the Commercial Real Estate Sector written by the MIT Community Innovators Lab and the Institute for Market Transformation.
A series of archived presentations from webinars sponsored by the U.S. Department of Energy Technical Assistance Program (TAP). opics include: strategic energy planning, policies and programs, data management and evaluation, financing solutions, and energy technologies. To find revolving loan fund webinars, use the search feature on the top right of the table.
A listing of past loan loss reserve fund webinars and associated files from Department of Energy's Technical Assistance Program. Topics include: strategic energy planning, policies and programs, data management and evaluation, financing solutions, and energy technologies. To find loan loss reserve fund webinars, use the search feature on the top right of the table.
This checklist will assist facility managers and building owners evaluate the capabilities of HVAC companies and the proposals they submit for installation of new HVAC equipment. The questions on the checklist will help owners and managers understand the requirements contained within the ACCA HVAC quality installation Standard 5.
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.
There are over 200 energy efficiency loan programs—across 49 U.S. states—administered by utilities, state/local government agencies, or private lenders. This distributed model has led to significant variation in program design and implementation practices including how data is collected and used.
The objective of this report is to take a foundational step towards the establishment of common data collection practices for energy efficiency lending. We review existing practices for data collection for energy efficiency financing programs and, based on discussions with various stakeholders, identify high-priority needs, characterize potential uses for finance program data, and identify use cases that describe how stakeholders use data for key objectives and actions. We address the following topics:
• Rationales for collecting more consistent data from energy efficiency finance programs;
• Identification and discussion of energy efficiency finance program use cases;
• Challenges with collecting information from customers that participate in finance programs; and
• Issues with data collection and aggregation across multiple finance programs.
The Smart Monitoring and Diagnostic System (SMDS) is a low-cost technology that helps building owners and managers keep rooftop air conditioner and heat pump units (RTUs) operating properly at peak efficiency. The SMDS technology has the potential to significantly benefit small commercial buildings, which predominately use RTUs for space conditioning. Through the Better Buildings Alliance, a field demonstration was conducted at four sites using two SMDS prototypes. This case study provides a summary of the field demonstration results.
The full report is available at: https://buildingdata.energy.gov/cbrd/resource/1927
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.