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.
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
In 2011, the U.S. Department of Energy’s Building Technology Office (DOE’s BTO), with help from the Better Buildings Alliance (BBA) members, developed a specification (RTU Challenge) for high performance rooftop air-conditioning units (RTUs) with capacity ranges between 10 and 20 tons. Daikin’s Rebel RTU was recognized by DOE in May 2012 as the first to meet the RTU Challenge specifications. A study was commissioned to compare the Rebel unit with a standard reference unit in the field. The goal of the RTU Challenge demonstration was to estimate seasonal performance of the RTU Challenge unit and the annual savings that can be achieved by installing the Rebel unit instead of an alternate standard unit. This case study details this demonstration.
In this paper, we apply an automated whole-building M&V tool to historic data sets from energy efficiency programs to begin to explore the accuracy, cost, and time trade-offs between more traditional M&V, and these emerging streamlined methods that use high-resolution energy data and automated computational intelligence. The results show that 70% of the buildings were well suited to the automated approach. In a majority of the cases (80%) savings and uncertainties for each individual building were quantified to levels above the criteria in ASHRAE Guideline 14.
"The general concept of using meter data to quantify building energy savings is intuitive and straightforward; in practice, however, there are many complications. With support from DOE, LBNL has been working with partners to address many of the market and technical barriers for M&V 2.0."
This short blog article describes a related white paper titled "The Status and Promise of Advanced M&V: An Overview of 'M&V 2.0 Methods, Tools, and Applications" and a technical article titled "Application of Automated Measurement and Verification to Utility Energy Efficiency Program Data."
"The objective of this paper is to provide background information and frame key discussion points related to advanced M&V. The paper identifies the benefits, methods, and requirements of advanced M&V and outlines key technical issues for applying these methods. It presents an overview of the distinguishing elements of M&V 2.0 tools and of how the industry is addressing needs for tool testing, consistency, and standardization, and it identifies opportunities for collaboration."