Can analytics software measure end user computing electricity consumption?

Abstract The purpose of this research is to answer the question, ‘can analytics software measure end user computing electricity consumption?’ rationale being that success traditional methodologies, such as watt metres, limited by newly evolved barriers mobility and scale (Greenblatt et al., in Field data collection miscellaneous electrical loads Northern California: initial results. Ernest Orlando Lawrence Berkeley National Laboratory paper, pp 4–5, 2013). Such limitations significantly reduce availability use phase energy consumption field (Karpagam Yung, J Clean Prod 156:828, 2017). This causes computer manufacturers instead rely upon no-user present efficiency benchmarks (Energy Star, Product finder, product, certified computers, Washington, D.C.: United States Department Energy. https://www.energystar.gov/productfinder/product/certified-computers/results , 2021) act baseline for product carbon footprint reports. As benchmark approach previously tested cause scope 2 greenhouse gas emissions quantification be inaccurate − 48% + 107% (Sutton-Parker, Determining device Scope GHG with accurate measurement, 1877-0509. Amsterdam: Science Direct, Elsevier B.V., 2020), testing a new methodology includes impact human–computer interaction arguably value. such, proposed method using distributed node based capture both asset human profile sets from one hundred eleven users operating subject organisation 30-days. simple node, unlike metre, not restricted location, can deployed monitored globally centralised location move ensure constant measurement. resulting are used populate current calculation flow (Kawamoto Energy 27:255, 2001; Roth office telecommunications equipment commercial buildings: baseline, 2002) order examine omissions. Additionally, test accuracy, acts control subject, measuring watt-metre software. extensively proven star computers final version 8.0 Specification, Washington D.C., https://www.energystar.gov/products/spec/computers_version_8_0_pd 2020) will therefore expose errors produced relation power draw, on-time kilo-watt hours (kWh) values. Further period, findings mixed. Positively, overcomes numerous, assorted devices (scale) ever changing locations (mobility). achieved reporting real-time make model together specific via internet technologies. Negatively, identifies values relatively 48%. Furthermore, omissions experienced including exclusion displays caused requiring an system collect data. latter point would exclude measurement concomitant any connected desktop or mobile computers. Consequently, whilst question answered, identification exaggerating excluding peripheral devices, determines need further development. key lifecycle assessment accounting protocol require high levels accuracy (WBCSD WRI, protocol. A corporate standard, Geneva, Switzerland New York, USA. https://ghgprotocol.org/corporate-standard 2004). It recommended undertaken specifically address over aspect identified algorithms calculate hardware draw. Graphical abstract