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 Energy Management Package was developed by LBNL and DOE to deliver energy management and low- and no-cost energy efficiency opportunities to the small commercial building sector (less than 50,000 sq. ft.). This whole-building efficiency service offering was designed to be delivered by HVAC contractors at low transaction cost, and includes analysis of whole-building monthly or interval energy data and benchmarking, using free and low cost software tools. The website includes links to the Package itself, the business model associated with delivery of the Package, an introductory webinar, and an overview slide deck. Contractors servicing the small commercial sector who are interested to help demonstrate this approach should contact the point of contact below.
The package helps contractors to address questions such as:
What no- or low-cost measures could generate savings in a building?
How much energy does a building use compared with similar buildings?
How has energy usage changed over time? If the owner has already made upgrades, have they been effective?
How much money could potentially be saved through energy upgrades?
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.