Office technology energy use and savings potential in New York. Final report
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The purpose of this research is to answer the question, ‘can analytics software measure end user computing electricity consumption?’ The rationale being that the success of traditional methodologies, such as watt metres, is limited by newly evolved barriers such as mobility and scale (Greenblatt et al., in Field data collection of miscellaneous electrical loads in Northern California: initial results. Ernest Orlando Lawrence Berkeley National Laboratory research paper, pp 4–5, 2013). Such limitations significantly reduce the availability of end user computing use phase energy consumption field data (Karpagam and Yung, in J Clean Prod 156:828, 2017). This causes computer manufacturers to instead rely upon no-user present energy efficiency benchmarks (Energy Star, in Product finder, product, certified computers, results. Washington, D.C.: United States Department of Energy. https://www.energystar.gov/productfinder/product/certified-computers/results, 2021) to act as baseline data for product carbon footprint reports. As the benchmark approach is previously tested to cause scope 2 greenhouse gas emissions quantification to be inaccurate by − 48% to + 107% (Sutton-Parker, in Determining end user computing device Scope 2 GHG emissions with accurate use phase energy consumption measurement, 1877-0509. Amsterdam: Science Direct, Elsevier B.V., 2020), testing a new methodology that includes the impact of human–computer interaction is arguably of value. As such, the proposed method is tested using a distributed node based analytics software to capture both computer asset and human use profile data sets from one hundred and eleven computer users operating in a subject organisation for 30-days. The simple rationale is that the node, unlike a watt metre, is not restricted by location, can be deployed and monitored globally from a centralised location and can move with the computer to ensure constant measurement. The resulting data sets are used to populate a current use phase electricity consumption calculation data flow (Kawamoto et al., in Energy 27:255, 2001; Roth et al., in Energy consumption by office and telecommunications equipment in commercial buildings: energy consumption baseline, 2002) in order to examine for omissions. Additionally, to test for data accuracy, one computer user acts as a control subject, measuring electricity consumption with both a watt-metre and the analytics software. The rationale being that the watt-metre data is extensively proven to be accurate (Energy Star, in Energy star computers final version 8.0 Specification, Washington D.C., United States Department of Energy. https://www.energystar.gov/products/spec/computers_version_8_0_pd, 2020) and will therefore expose errors produced by the software in relation to power draw, on-time and resulting kilo-watt hours (kWh) values. Further to the data capture period, the findings are mixed. Positively, the new method overcomes the barriers of numerous, assorted devices (scale) operating in ever changing locations (mobility). This is achieved by the node reporting in real-time make and model asset data together with device specific electricity consumption and location data via internet technologies. Negatively, the control subject identifies that the electricity consumption values produced by the software are inaccurate by a relatively constant 48%. Furthermore, data omissions are experienced including the exclusion of computer displays caused by the node requiring an operating system to collect data. This latter point would exclude the energy consumption measurement and therefore concomitant greenhouse gas emissions of any displays connected to desktop or mobile computers. Consequently, whilst the research question is answered, the identification of the software exaggerating use phase energy consumption by 48% and excluding peripheral devices, determines the analytics methodology to be in need of further development. The rationale being that use phase consumption quantification is key to lifecycle assessment and greenhouse gas accounting protocol and both require high levels of accuracy (WBCSD and WRI, in The greenhouse gas protocol. A corporate accounting and reporting standard, Geneva, Switzerland and New York, USA. https://ghgprotocol.org/corporate-standard, 2004). It is therefore recommended that further research be undertaken to specifically address omissions and to reduce the over reporting aspect identified as caused by algorithms in the software used to calculate hardware power draw. Graphical abstract
The purpose of this research is to answer the question, ‘can analytics software measure end user computing electricity consumption?’ The rationale being that the success of traditional methodologies, such as watt metres, is limited by newly evolved barriers such as mobility and scale (Greenblatt et al., in Field data collection of miscellaneous electrical loads in Northern California: initial results. Ernest Orlando Lawrence Berkeley National Laboratory research paper, pp 4–5, 2013). Such limitations significantly reduce the availability of end user computing use phase energy consumption field data (Karpagam and Yung, in J Clean Prod 156:828, 2017). This causes computer manufacturers to instead rely upon no-user present energy efficiency benchmarks (Energy Star, in Product finder, product, certified computers, results. Washington, D.C.: United States Department of Energy. https://www.energystar.gov/productfinder/product/certified-computers/results, 2021) to act as baseline data for product carbon footprint reports. As the benchmark approach is previously tested to cause scope 2 greenhouse gas emissions quantification to be inaccurate by − 48% to + 107% (Sutton-Parker, in Determining end user computing device Scope 2 GHG emissions with accurate use phase energy consumption measurement, 1877-0509. Amsterdam: Science Direct, Elsevier B.V., 2020), testing a new methodology that includes the impact of human–computer interaction is arguably of value. As such, the proposed method is tested using a distributed node based analytics software to capture both computer asset and human use profile data sets from one hundred and eleven computer users operating in a subject organisation for 30-days. The simple rationale is that the node, unlike a watt metre, is not restricted by location, can be deployed and monitored globally from a centralised location and can move with the computer to ensure constant measurement. The resulting data sets are used to populate a current use phase electricity consumption calculation data flow (Kawamoto et al., in Energy 27:255, 2001; Roth et al., in Energy consumption by office and telecommunications equipment in commercial buildings: energy consumption baseline, 2002) in order to examine for omissions. Additionally, to test for data accuracy, one computer user acts as a control subject, measuring electricity consumption with both a watt-metre and the analytics software. The rationale being that the watt-metre data is extensively proven to be accurate (Energy Star, in Energy star computers final version 8.0 Specification, Washington D.C., United States Department of Energy. https://www.energystar.gov/products/spec/computers_version_8_0_pd, 2020) and will therefore expose errors produced by the software in relation to power draw, on-time and resulting kilo-watt hours (kWh) values. Further to the data capture period, the findings are mixed. Positively, the new method overcomes the barriers of numerous, assorted devices (scale) operating in ever changing locations (mobility). This is achieved by the node reporting in real-time make and model asset data together with device specific electricity consumption and location data via internet technologies. Negatively, the control subject identifies that the electricity consumption values produced by the software are inaccurate by a relatively constant 48%. Furthermore, data omissions are experienced including the exclusion of computer displays caused by the node requiring an operating system to collect data. This latter point would exclude the energy consumption measurement and therefore concomitant greenhouse gas emissions of any displays connected to desktop or mobile computers. Consequently, whilst the research question is answered, the identification of the software exaggerating use phase energy consumption by 48% and excluding peripheral devices, determines the analytics methodology to be in need of further development. The rationale being that use phase consumption quantification is key to lifecycle assessment and greenhouse gas accounting protocol and both require high levels of accuracy (WBCSD and WRI, in The greenhouse gas protocol. A corporate accounting and reporting standard, Geneva, Switzerland and New York, USA. https://ghgprotocol.org/corporate-standard, 2004). It is therefore recommended that further research be undertaken to specifically address omissions and to reduce the over reporting aspect identified as caused by algorithms in the software used to calculate hardware power draw. Graphical abstract
ENERGY STAR is a voluntary labeling program designed to identify and promote energy-e$cient products. Operated jointly by the Environmental Protection Agency (EPA) and the US Department of Energy (DOE), ENERGY STAR labels exist for more than 20 products, spanning o$ce equipment, residential heating and cooling equipment, new homes, commercial and residential lighting, home electronics, and major appliances. We present estimates of the energy, dollar and carbon savings already achieved by the program and provide savings forecasts for several market penetration scenarios for the period 2001}2010.The target market penetration forecast represents our best estimate of future ENERGY STAR savings. It is based on realistic market penetration goals for each of the products. We also provide a forecast under the assumption of 100% market penetration; that is, we assume that all purchasers buy ENERGY STAR-compliant products instead of standard e$ciency products throughout the analysis period. Finally, we assess the sensitivity of our target penetration case forecasts to greater or lesser marketing success by EPA and DOE, lower-than-expected future energy prices, and higher or lower rates of carbon emissions by electricity generators. Published by
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