17 Matching Results

Search Results

Advanced search parameters have been applied.

Self-benchmarking Guide for Laboratory Buildings: Metrics, Benchmarks, Actions

Description: This guide describes energy efficiency metrics and benchmarks that can be used to track the performance of and identify potential opportunities to reduce energy use in laboratory buildings. This guide is primarily intended for personnel who have responsibility for managing energy use in existing laboratory facilities - including facilities managers, energy managers, and their engineering consultants. Additionally, laboratory planners and designers may also use the metrics and benchmarks described in this guide for goal-setting in new construction or major renovation. This guide provides the following information: (1) A step-by-step outline of the benchmarking process. (2) A set of performance metrics for the whole building as well as individual systems. For each metric, the guide provides a definition, performance benchmarks, and potential actions that can be inferred from evaluating this metric. (3) A list and descriptions of the data required for computing the metrics. This guide is complemented by spreadsheet templates for data collection and for computing the benchmarking metrics. This guide builds on prior research supported by the national Laboratories for the 21st Century (Labs21) program, supported by the U.S. Department of Energy and the U.S. Environmental Protection Agency. Much of the benchmarking data are drawn from the Labs21 benchmarking database and technical guides. Additional benchmark data were obtained from engineering experts including laboratory designers and energy managers.
Date: July 13, 2009
Creator: Mathew, Paul; Greenberg, Steve & Sartor, Dale
Partner: UNT Libraries Government Documents Department

Self-benchmarking Guide for Data Centers: Metrics, Benchmarks, Actions

Description: This guide describes energy efficiency metrics and benchmarks that can be used to track the performance of and identify potential opportunities to reduce energy use in data centers. This guide is primarily intended for personnel who have responsibility for managing energy use in existing data centers - including facilities managers, energy managers, and their engineering consultants. Additionally, data center designers may also use the metrics and benchmarks described in this guide for goal-setting in new construction or major renovation. This guide provides the following information: (1) A step-by-step outline of the benchmarking process. (2) A set of performance metrics for the whole building as well as individual systems. For each metric, the guide provides a definition, performance benchmarks, and potential actions that can be inferred from evaluating this metric. (3) A list and descriptions of the data required for computing the metrics. This guide is complemented by spreadsheet templates for data collection and for computing the benchmarking metrics. This guide builds on prior data center benchmarking studies supported by the California Energy Commission. Much of the benchmarking data are drawn from the LBNL data center benchmarking database that was developed from these studies. Additional benchmark data were obtained from engineering experts including facility designers and energy managers. This guide also builds on recent research supported by the U.S. Department of Energy's Save Energy Now program.
Date: July 13, 2009
Creator: Mathew, Paul; Ganguly, Srirupa; Greenberg, Steve & Sartor, Dale
Partner: UNT Libraries Government Documents Department

How Does Your Data Center Measure Up? Energy Efficiency Metrics and Benchmarks for Data Center Infrastructure Systems

Description: Data centers are among the most energy intensive types of facilities, and they are growing dramatically in terms of size and intensity [EPA 2007]. As a result, in the last few years there has been increasing interest from stakeholders - ranging from data center managers to policy makers - to improve the energy efficiency of data centers, and there are several industry and government organizations that have developed tools, guidelines, and training programs. There are many opportunities to reduce energy use in data centers and benchmarking studies reveal a wide range of efficiency practices. Data center operators may not be aware of how efficient their facility may be relative to their peers, even for the same levels of service. Benchmarking is an effective way to compare one facility to another, and also to track the performance of a given facility over time. Toward that end, this article presents the key metrics that facility managers can use to assess, track, and manage the efficiency of the infrastructure systems in data centers, and thereby identify potential efficiency actions. Most of the benchmarking data presented in this article are drawn from the data center benchmarking database at Lawrence Berkeley National Laboratory (LBNL). The database was developed from studies commissioned by the California Energy Commission, Pacific Gas and Electric Co., the U.S. Department of Energy and the New York State Energy Research and Development Authority.
Date: April 1, 2009
Creator: Mathew, Paul; Greenberg, Steve; Ganguly, Srirupa; Sartor, Dale & Tschudi, William
Partner: UNT Libraries Government Documents Department

Labs21 sustainable design programming checklist version 1.0

Description: This checklist of sustainable design objectives and strategies can be used in the programming and conceptual design phases of a laboratory project. It includes the following: (1) Brief descriptions of each objective and strategy. (2) Metrics for each objective. This checklist is primarily to be used by owners, architects and engineers during the programming and conceptual design phase of a project. It is especially appropriate for use in design charrettes. The strategies and metrics can be included as requirements in the programming document or can be identified for further analysis or consideration during the design development phase. This checklist is hierarchically organized into design areas, objectives for each design area, and strategies and metrics for each objective. The design areas generally correspond to the design areas of the LEED(TM) rating system from the U.S. Green Building Council.
Date: January 7, 2005
Creator: Mathew, Paul & Greenberg, Steve
Partner: UNT Libraries Government Documents Department

Energy Efficiency Building Code for Commercial Buildings in Sri Lanka

Description: 1.1.1 To encourage energy efficient design or retrofit of commercial buildings so that they may be constructed, operated, and maintained in a manner that reduces the use of energy without constraining the building function, the comfort, health, or the productivity of the occupants and with appropriate regard for economic considerations. 1.1.2 To provide criterion and minimum standards for energy efficiency in the design or retrofit of commercial buildings and provide methods for determining compliance with them. 1.1.3 To encourage energy efficient designs that exceed these criterion and minimum standards.
Date: September 30, 2000
Creator: Busch, John; Greenberg, Steve; Rubinstein, Francis; Denver, Andrea; Rawner, Esther; Franconi, Ellen et al.
Partner: UNT Libraries Government Documents Department

Data Center Energy Benchmarking: Part 2 - Case Studies on TwoCo-location Network Data Centers (No. 18 and 19)

Description: Two data centers in this study were within a co-location facility located on the sixth floor of a multi-story building in downtown Los Angeles, California. The facility had 37,758 gross square feet floor area with 2-foot raised-floors in the data services area. The two data centers were designated as the west data center (DC No.18) and the east data center (DC No.19). The study found that 56% of the overall electric power was consumed by sixth floor critical loads in both data centers, 33% of the power was consumed by HVAC systems, 3% of the power was consumed by UPS units, 3% of the power was for generator losses, and the remaining 5% was used by lighting and miscellaneous loads in the building. The power density of installed computer loads (rack load) in the two data centers was 20 W/ft{sup 2} and 56 W/ft{sup 2}, respectively. The power density was relatively lower in DC No.18 compared to other data centers previously studied. In addition, HVAC to IT power demand ratio was 0.6 in DC No.18 in this study, and was 0.4 in DC No.19. Two out of three chillers were running at a low partial load, making the operation very energy inefficient. The operation and control of the chillers and air-handling units should be optimized while providing sufficient cooling to the data centers. Although arranging hot aisle/cold aisle design to separate airflow streams would be difficult in such a co-location data center, optimizing air distribution should be pursued. General recommendations for improving overall data center energy efficiency include improving the design, operation, and control of mechanical systems serving the data centers with various critical loads in place. This includes chiller operation, chilled water system, AHUs, airflow management and control in data centers. Additional specific recommendations or considerations to improve energy ...
Date: August 1, 2007
Creator: Xu, Tengfang & Greenberg, Steve
Partner: UNT Libraries Government Documents Department

Data Center Energy Benchmarking: Part 3 - Case Study on an ITEquipment-testing Center (No. 20)

Description: The data center in this study had a total floor area of 3,024 square feet (ft{sup 2}) with one-foot raised-floors. It was a rack lab with 147 racks, and was located in a 96,000 ft{sup 2} multi-story office building in San Jose, California. Since the data center was used only for testing equipment, it was not configured as a critical facility in terms of electrical and cooling supply. It did not have a dedicated chiller system but was served by the main building chiller plant and make-up air system. Additionally it was served by only a single electrical supply with no provision for backup power in the event of a power outage. The Data Center operated on a 24 hour per day, year-round cycle, and users had full-hour access to the data center facility. The study found that data center computer load accounted for 15% of the overall building electrical load, while the total power consumption attributable to the data center including allocated cooling load and lighting was 22% of the total facility load. The density of installed computer loads (rack load) in the data center was 61 W/ft{sup 2}. Power consumption density for all data center allocated load (including cooling and lighting) was 88 W/ft{sup 2}, approximately eight times the average overall power density in rest of the building (non-data center portion). The building and its data center cooling system was provided with various energy optimizing systems that included the following: (1) Varying chilled water flow rate through variable speed drives on the primary pumps. (2) No energy losses due to nonexistence of UPS or standby generators. (3) Minimized under-floor obstruction that affects the delivery efficiency of supply air. (4) Elimination of dehumidification/humidification within the CRAH units. For the data center, 70% of the overall electric power was the ...
Date: July 1, 2007
Creator: Xu, Tengfang & Greenberg, Steve
Partner: UNT Libraries Government Documents Department

Data Center Energy Benchmarking: Part 4 - Case Study on aComputer-testing Center (No. 21)

Description: The data center in this study had a total floor area of 8,580 square feet (ft{sup 2}) with one-foot raised-floors. It was a rack lab with 440 racks, and was located in a 208,240 ft{sup 2} multi-story office building in San Jose, California. Since the data center was used only for testing equipment, it was not configured as a critical facility in terms of electrical and cooling supply. It did not have a dedicated chiller system but served by the main building chiller plant and make-up air system. Additionally, it was served by a single electrical supply with no provision for backup power. The data center operated on a 24 hour per day, year-round cycle, and users had all hour full access to the data center facility. The study found that data center computer load accounted for 23% of the overall building electrical load, while the total power consumption attributable to the data center including allocated cooling load and lighting was 30% of the total facility load. The density of installed computer loads (rack load) in the data center was 63 W/ft{sup 2}. Power consumption density for all data center allocated load (including cooling and lighting) was 84 W/ft{sup 2}, approximately 12 times the average overall power density in rest of the building (non-data center portion). For the data center, 75% of the overall electric power was the rack critical loads, 11% of the power was consumed by chillers, 9% by CRAH units, 1% by lighting system, and about 4% of the power was consumed by pumps. The ratio of HVAC to IT power demand in the data center in this study was approximately 0.32. General recommendations for improving overall data center energy efficiency include improving the lighting control, airflow optimization, and control of mechanical systems serving the data center ...
Date: August 1, 2007
Creator: Xu, Tengfang & Greenberg, Steve
Partner: UNT Libraries Government Documents Department

Metrics and Benchmarks for Energy Efficiency in Laboratories

Description: A wide spectrum of laboratory owners, ranging from universities to federal agencies, have explicit goals for energy efficiency in their facilities. For example, the Energy Policy Act of 2005 (EPACT 2005) requires all new federal buildings to exceed ASHRAE 90.1-2004 [1] by at least 30%. A new laboratory is much more likely to meet energy efficiency goals if quantitative metrics and targets are specified in programming documents and tracked during the course of the delivery process. If not, any additional capital costs or design time associated with attaining higher efficiencies can be difficult to justify. This article describes key energy efficiency metrics and benchmarks for laboratories, which have been developed and applied to several laboratory buildings--both for design and operation. In addition to traditional whole building energy use metrics (e.g. BTU/ft{sup 2}.yr, kWh/m{sup 2}.yr), the article describes HVAC system metrics (e.g. ventilation W/cfm, W/L.s{sup -1}), which can be used to identify the presence or absence of energy features and opportunities during design and operation.
Date: April 10, 2008
Creator: Engineers, Rumsey; Mathew, Paul; Mathew, Paul; Greenberg, Steve; Sartor, Dale; Rumsey, Peter et al.
Partner: UNT Libraries Government Documents Department

Right-Sizing Laboratory Equipment Loads

Description: Laboratory equipment such as autoclaves, glass washers, refrigerators, and computers account for a significant portion of the energy use in laboratories. However, because of the general lack of measured equipment load data for laboratories, designers often use estimates based on 'nameplate' rated data, or design assumptions from prior projects. Consequently, peak equipment loads are frequently overestimated. This results in oversized HVAC systems, increased initial construction costs, and increased energy use due to inefficiencies at low part-load operation. This best-practice guide first presents the problem of over-sizing in typical practice, and then describes how best-practice strategies obtain better estimates of equipment loads and right-size HVAC systems, saving initial construction costs as well as life-cycle energy costs. This guide is one in a series created by the Laboratories for the 21st Century ('Labs21') program, a joint program of the U.S. Environmental Protection Agency and U.S. Department of Energy. Geared towards architects, engineers, and facilities managers, these guides provide information about technologies and practices to use in designing, constructing, and operating safe, sustainable, high-performance laboratories.
Date: November 29, 2005
Creator: Frenze, David; Greenberg, Steve; Mathew, Paul; Sartor, Dale & Starr, William
Partner: UNT Libraries Government Documents Department

Toward the Holy Grail of Perfect Information: Lessons Learned Implementing an Energy Information System in a Commercial Building

Description: Energy information systems (real-time acquisition, analysis, and presentation of information from energy end-uses) in commercial buildings have demonstrated value as tools for improving energy efficiency and thermal comfort. These improvements include characterization through benchmarking, identification of retrofit opportunities, anomaly detection to inform retro-commissioning, and feedback to occupants to encourage shifts in behavior. Energy information systems can play a vital role in achieving a variety of ambitious sustainability goals for the existing stock of commercial buildings, but their implementation is often fraught with pitfalls. In this paper, we present a case study of an EIS and sub-metering project executed in a representative commercial office building. We describe the building, highlight a few of its problems, and detail the hardware and software technologies we employed to address them. We summarize the difficulties encountered and lessons learned, and suggest general guidelines for future EIS projects to improve performance and save energy in the commercial building fleet. These guidelines include measurement criteria, monitoring strategies, and analysis methods. In particular, we propose processes for: - Defining project goals, - Selecting end-use targets and depth of metering, - Selecting contractors and software vendors, - Installing and networking measurement devices, - Commissioning and using the energy information system.
Date: May 14, 2010
Creator: Kircher, Kevin; Ghatikar, Girish; Greenberg, Steve; Watson, Dave; Diamond, Rick; Sartor, Dale et al.
Partner: UNT Libraries Government Documents Department

Using measured equipment load profiles to 'right-size' HVACsystems and reduce energy use in laboratory buildings (Pt. 2)

Description: There is a general paucity of measured equipment load datafor laboratories and other complex buildings and designers often useestimates based on nameplate rated data or design assumptions from priorprojects. Consequently, peak equipment loads are frequentlyoverestimated, and load variation across laboratory spaces within abuilding is typically underestimated. This results in two design flaws.Firstly, the overestimation of peak equipment loads results in over-sizedHVAC systems, increasing initial construction costs as well as energy usedue to inefficiencies at low part-load operation. Secondly, HVAC systemsthat are designed without accurately accounting for equipment loadvariation across zones can significantly increase simultaneous heatingand cooling, particularly for systems that use zone reheat fortemperature control. Thus, when designing a laboratory HVAC system, theuse of measured equipment load data from a comparable laboratory willsupport right-sizing HVAC systems and optimizing their configuration tominimize simultaneous heating and cooling, saving initial constructioncosts as well as life-cycle energy costs.In this paper, we present datafrom recent studies to support the above thesis. We first presentmeasured equipment load data from two sources: time-series measurementsin several laboratory modules in a university research laboratorybuilding; and peak load data for several facilities recorded in anational energy benchmarking database. We then contrast this measureddata with estimated values that are typically used for sizing the HVACsystems in these facilities, highlighting the over-sizing problem. Next,we examine the load variation in the time series measurements and analyzethe impact of this variation on energy use, via parametric energysimulations. We then briefly discuss HVAC design solutions that minimizesimultaneous heating and cooling energy use.
Date: June 29, 2005
Creator: Mathew, Paul; Greenberg, Steve; Frenze, David; Morehead, Michael; Sartor, Dale & Starr, William
Partner: UNT Libraries Government Documents Department

Design intent tool: User guide

Description: This database tool provides a structured approach to recording design decisions that impact a facility's design intent in areas such as energy efficiency.Owners and de signers alike can plan, monitor and verify that a facility's design intent is being met during each stage of the design process. Additionally, the Tool gives commissioning agents, facility operators and future owners and renovators an understanding of how the building and its subsystems are intended to operate, and thus track and benchmark performance.
Date: August 23, 2002
Creator: Mills, Evan; Abell, Daniel; Bell, Geoffrey; Faludi, Jeremy; Greenberg, Steve; Hitchcock, Rob et al.
Partner: UNT Libraries Government Documents Department

"Hot" for Warm Water Cooling

Description: Liquid cooling is key to reducing energy consumption for this generation of supercomputers and remains on the roadmap for the foreseeable future. This is because the heat capacity of liquids is orders of magnitude larger than that of air and once heat has been transferred to a liquid, it can be removed from the datacenter efficiently. The transition from air to liquid cooling is an inflection point providing an opportunity to work collectively to set guidelines for facilitating the energy efficiency of liquid-cooled High Performance Computing (HPC) facilities and systems. The vision is to use non-compressor-based cooling, to facilitate heat re-use, and thereby build solutions that are more energy-efficient, less carbon intensive and more cost effective than their air-cooled predecessors. The Energy Efficient HPC Working Group is developing guidelines for warmer liquid-cooling temperatures in order to standardize facility and HPC equipment, and provide more opportunity for reuse of waste heat. This report describes the development of those guidelines.
Date: August 26, 2011
Creator: Corporation, IBM; Group, Energy Efficient HPC Working; Corporation, Hewlett Packard; SGI; Inc., Cray; Corporation, Intel et al.
Partner: UNT Libraries Government Documents Department

Defining a Standard Metric for Electricity Savings

Description: The growing investment by governments and electric utilities in energy efficiency programs highlights the need for simple tools to help assess and explain the size of the potential resource. One technique that is commonly used in this effort is to characterize electricity savings in terms of avoided power plants, because it is easier for people to visualize a power plant than it is to understand an abstraction such as billions of kilowatt-hours. Unfortunately, there is no standardization around the characteristics of such power plants. In this letter we define parameters for a standard avoided power plant that have physical meaning and intuitive plausibility, for use in back-of-the-envelope calculations. For the prototypical plant this article settles on a 500 MW existing coal plant operating at a 70percent capacity factor with 7percent T&D losses. Displacing such a plant for one year would save 3 billion kW h per year at the meter and reduce emissions by 3 million metric tons of CO2 per year. The proposed name for this metric is the Rosenfeld, in keeping with the tradition among scientists of naming units in honor of the person most responsible for the discovery and widespread adoption of the underlying scientific principle in question--Dr. Arthur H. Rosenfeld.
Date: March 1, 2009
Creator: Brown, Marilyn; Akbari, Hashem; Blumstein, Carl; Koomey, Jonathan; Brown, Richard; Calwell, Chris et al.
Partner: UNT Libraries Government Documents Department