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 Standard Energy Efficiency Data (SEED) Platform was developed to help state and local governments address the challenge of implement building performance reporting regulations for private and/or public buildings. SEED provides a flexible, free, secure, and private data platform for managing large datasets. The SEED source code is open source and extensible so that other parties can access the data, and offer add-on tools and services in a replicable way. This paper details the varying processes that had started to emerge in New York City, Seattle, Washington DC, San Francisco, and Austin, and then summarize the features of SEED that were developed to address key challenges. SEED has the potential to significantly decrease the administrative effort required to implement performance-tracking programs and increase the quality of analysis. By aligning data formats and data management processes across jurisdictions, SEED can also help to ease reporting burdens for owners and contractors, facilitate parallel analysis and comparisons between jurisdictions, and increase the availability of products and services that utilize this data. This paper also explores SEED’s potential at scale in the market and the ongoing role for interested users and software developers to contribute resources and provide input on ongoing development.
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.
An increasing number of state and local jurisdictions are implementing building performance reporting laws, which generate large quantities of useful data on the characteristics and resource consumption of the building stock. However, to realize the potential of these policies, the data must not only be disclosed, but put to work to drive energy savings. Under a three-year pilot, Washington DC (DC), New York City (NYC) and their partners are pioneering the use of data from building performance reporting in energy efficiency programs. To minimize the administrative burden of managing, combining, and sharing these data sets, the cities are utilizing the U.S. Department of Energy’s (DOE) open-source Standard Energy Efficiency Data (SEED) Platform.
The Putting Data to Work project team is working with efficiency program administrators to develop and implement new and innovative ways in which the data collected through benchmarking, energy audits, and related policies can be used to improve energy policies and planning, unlock data directly for market use, scale-up the market for energy efficiency services, drive competition, better target utility incentive programs, and inform measurement and verification.
This paper details achievements and key findings in DC and NYC to date, including the importance of high compliance, data quality, and data cleansing in using the information collected; methods that the cities are using to apply data to drive maximum energy efficiency; and the importance of inter- and intra-agency collaboration in program success. The paper also outlines the path forward and details expected outcomes and scalability of project activities.
This article appears in the July 2016 issue of the ASHRAE Journal (pgs. 38-45). Brief summary:
The U.S. Department of Energy's Building Performance Database (BPD) is the largest publicly available data source for energy-related characteristics of commercial and residential buildings in the United States, collected from federal, state, and local governments, utilities, and private companies. The BPD provides anonymized building energy use and asset data with analytical capabilities to help energy service providers, real estate owners and managers, policy makers, and energy consultants make decisions about energy efficiency and retrofit projects.
This article examines some of the promises and perils of having large amounts of building data at the user's fingertips and how to use such data and statistical analysis tools effectively to support decision-making by energy professionals.
A net zero-energy community (ZEC) is one that has greatly reduced energy needs through efficiency gains such that the balance of energy for vehicles, thermal, and electrical energy within the community is met by renewable energy. Past work resulted in a common zero-energy building (ZEB) definition system of “zero energy” and a classification system for ZEBs based on the renewable energy sources used by a building. This paper begins with a focus solely on buildings and expands the concept to define a zero-energy community, applying the ZEB hierarchical renewable classification system to the concept of community. A community that offsets all of its energy use from renewables available within the community’s built environment and unusable brownfield sites is at the top of the ZEC classification system at a ZEC of A. (A brownfield site is where the redevelopment or reuse may be complicated by the presence or potential presence of a hazardous substance, pollutant or contaminant.) A community that achieves a ZEC definition primarily through the purchase of new off-site, Renewable Energy Certificates (RECs) is placed at the lowest end of the ZEC classification but is still considered a good achievement.
Power purchase agreement presentation for government buildings under the federal energy management program (FEMP).
The General Service Administration's (GSA) Green Proving Ground (GPG) program worked with a team from the National Renewable Energy Laboratory (NREL) to identify buildings with office setups and equipment distributions typical of the wider GSA building stock. Eight buildings from GSA’s Mid-Atlantic Region, where plug loads average 21%, were selected. In each building, approximately 12 standard power strips with no control capability (the incumbent technology) were replaced with APSs, which monitored and provided power to an array of devices. More than 295 devices were monitored during the study, which consisted of three separate test periods, each four weeks in length. All buildings selected had workstation power management in place.
Work with the buildings industry to develop and maintain data, methods, and tools to understand and improve the sustainability of buildings at a fundamental level and continue to develop and support the U.S. Life Cycle Inventory (LCI) Database, which provides the starting point for LCA.