Benchmarking Energy Use in Commercial Buildings Satish Kumar, Ph.D. Lawrence Berkeley National Laboratory Public Sector Energy Management Workshop Mumbai, India September 20, 2005

Objective Understand Energy Efficiency Benchmarking and the different types of benchmarking Study and analyze existing benchmarking tools Explore the various parameters and techniques used in benchmarking Demonstrate detailed assessment of specific tools through the application of a hypothetical case study The study only aims to examine and present observations from analysis of benchmarking tools from User standpoint and does not look into technical aspects.

Purpose of Benchmarking Assessing the energy performance of buildings to an established standard or baseline Acts as a first step for energy audits, optimizing building energy use and implementing energy savings measures. Different for different user group type –  Building owners/facility managers Compare buildings’ energy use and performance with other buildings.  Utility companies Track energy use and combine energy data from various buildings  ESCOs and Building Energy Auditors Communicate energy performance of buildings in terms of “typical” vs. “best practice” benchmark

Benefits of Benchmarking Determining how a building’s energy use compares with others Setting targets for improved performance and monitoring them on an ongoing basis Facilitating assessment of property value Gaining recognition for exemplary achievement Identifying energy saving strategies

Benchmarking in the Indian Context Current Status  TEDDY Database by The Energy Research Institute No commercial building information  Other efforts? Barriers  Absence of standardized process and organizations Need to work with Utilities and SEBs  Lack of perceived benefits to the party supplying data  Data resolution and quality

Types of Benchmarking Internal Benchmarking Comparison of energy performance of buildings against itself (over a period of time) or between a group of similar buildings Typically used to compare performance before and after retrofit measures have been taken External Benchmarking Comparison of energy performance of buildings. against standards or baseline as established part of benchmarking tool Typically used as a method to set targets for future performance.

Evaluation of Existing Benchmarking Tools Based on literature and web search – 19 benchmarking tools were identified and studied Tools selected were from different locations, bldg types, varying benchmarking methods and different output results Were tested and analyzed from a User stand point Main features and strengths and weaknesses of each were identified. North America Arch Cal Arch e-Review EMCOR Energy Edge CustomNet E-Benchmark GB Tool Green Globes Canada Labs 21 One-2-Five Utiligraph Energy Star ORNL’s Benchmarking tool for Office Buildings For Electric Fuel Use For Non-Electric Fuel Use Asia/ Pacific e-Energy e-Bench Energy Use Indicators and Benchmarks for Commercial Sector Europe BREEAM Action Energy Target Energy Services

Case Study Application and Analysis Hypothetical office building case study was developed for a comparative analysis 10 benchmarking tools were used for analysis. Tools chosen were web based and freely available A detailed framework was developed Analysis enabled to identify basic features addressed by all the tools

Key Factors to Consider in Benchmarking

Conclusions Flexible benchmarking opportunities are required to adapt based on specific building needs and systems Absence of a “clear winner” or “do-it-all” tool Recommendations can be targeted to two principal audiences namely –  User group - such as facility managers, building operators, owners who utilize these tools for analysis - Support and empower in their choice of benchmarking tools  Developer group –who design and develop similar benchmarking tools for energy efficiency calculation. - Suggest missing links and gaps in existing tools that need to be bridged

User Group Identify bldg information and data available for use  Critical for choosing the right tool  Dependent on user knowledge and available input data  Minimum input data available are area, location, annual/monthly utility bills for all fuels used Identify the purpose and type of benchmarking based on the output desired  Whole bldg analysis vs. system level  Dependent on function and complexities of building

User Group (contd.) Understand comparison basis  Examine the correlation of benchmarked buildings. with database buildings  Comparison of single fuel type (e.g. all electric) buildings with database consisting of mixed fuel type (e.g. gas &electric) can be misleading Understand distinction between EUI (energy use intensity) and Energy Efficiency ranking (scores or percentile)

Developer Group Advance benchmarking with action oriented guidelines  Evaluate output and provide measures to improve energy efficiency  Tailor action oriented guidelines based on the benchmarking output Provide benchmarking history of buildings with time for evaluating energy use and performance with time  Benchmarking evaluation and results may vary with increasing database  Analyze progress in energy usage and efficiency resulting from retrofit measures User interface  Incorporate a user guide to help users learn and understand the various features of the tool  Explain the terminology used, caveats about tool application and methods to interpret the out put results.

Policy Makers Determine the goals and objectives of benchmarking Decide on the methodology that would underpin the benchmarking analysis specific to the country/region after reviewing the pros and cons of existing benchmarking tools Identify the benchmarking data needed to achieve the goals and support the chosen methodology Identify sources of data and assign responsibilities for collecting and storing data in a consistent format Develop quality assurance and checks to maintain benchmarking data integrity Make the benchmarking tool available to key stakeholders and public

Credits This presentation is based on the research and analysis conducted by Lawrence Berkeley National Laboratory Special thanks to Priya Premchandran and Dr. Paul Mathew