Case study describing how adidas implemented a best practice of a planned replacement program for its rooftop units (RTUs), which resulted in significant cost and energy savings. The case study outlines the planning process, implementation, results, and the future plans of their RTU replacement program.
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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.
Best practices publication that shows the different stages in the design process, the important considerations for producing a low energy building, the tools that can be used at different points in the process, and the information that needs to be exchanged between different parts of an integrated project team at different stages.
This document lists a set of resources that can help small business owners make informed decisions about their energy use and identify opportunities for long-term financial savings from energy efficiency improvements. These resources include case studies, energy savings and investment calculators, technical guides and information on state and federal incentives programs.
Over the course of 5 years, NREL worked with commercial building owners and their design teams in the DOE Commercial Building Partnerships (CBP) to cut energy consumption by 50% in new construction (versus code) and by 30% in existing building pilot projects (versus code or pre-retrofit operational energy use depending on the preference of the Partner) using strategies that could be replicated across their building portfolios. A number of different building types were addressed, including supermarket, retail merchandise, combination big box (general merchandise and food sales), high rise office space, and warehouse. The projects began in pre-design and included a year of measurement data to evaluate performance against design expectations. Focused attention was required throughout the entire process to achieve a design with the potential to hit the energy performance target and to operate the resulting building to reach this potential. This paper will report quantitative results and cover both the technical and the human sides of CBP, including the elements that were required to succeed and where stumbling blocks were encountered. It will also address the impact of energy performance goals and intensive energy modeling on the design process innovations and best practices.
This website provides guidance on metrics and benchmarks that can be used to identify potential efficiency opportunities in laboratories, data centers and cleanrooms. These "high-tech" buildings are very energy intensive have efficiency opportunities that are not typically found in other commercial buildings.
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.