02489nas a2200205   4500008004100000245008600041210006900127520180800196653001702004653002002021653002102041653001702062653001502079653003202094100001402126700001902140700001702159700002202176856008502198       2017                        eng d00aElectric Load Shape Benchmarking for Small- and Medium-Sized Commercial Buildings0 aElectric Load Shape Benchmarking for Small and MediumSized Comme3 a<p>Small- and medium-sized commercial buildings owners and utility managers often look for opportunities for energy cost savings through energy efficiency and energy waste minimization. However, they currently lack easy access to low-cost tools that help interpret the massive amount of data needed to improve understanding of their energy use behaviors. Benchmarking is one of the techniques used in energy audits to identify which buildings are priorities for an energy analysis. Traditional energy performance indicators, such as the energy use intensity (annual energy per unit of floor area), consider only the total annual energy consumption, lacking consideration of the fluctuation of energy use behavior over time, which reveals the time of use information and represents distinct energy use behaviors during different time spans. To fill the gap, this study developed a general statistical method using 24-hour electric load shape benchmarking to compare a building or business/tenant space against peers. Specifically, the study developed new forms of benchmarking metrics and data analysis methods to infer the energy performance of a building based on its load shape. We first performed a data experiment with collected smart meter data using over 2,000 small- and medium-sized businesses in California. We then conducted a cluster analysis of the source data, and determined and interpreted the load shape features and parameters with peer group analysis. Finally, we implemented the load shape benchmarking feature in an open-access web-based toolkit (the Commercial Building Energy Saver) to provide straightforward and practical recommendations to users. The analysis techniques were generic and flexible for future datasets of other building types and in other utility territories.</p>10abenchmarking10aBuilding energy10acluster analysis10aload profile10aload shape10arepresentative load pattern1 aLuo, Xuan1 aHong, Tianzhen1 aChen, Yixing1 aPiette, Mary, Ann  uhttps://simulationresearch.lbl.gov/publications/electric-load-shape-benchmarking02128nas a2200241   4500008003900000245007200039210006900111260002700180300001400207490000700221520132000228653003001548653003301578653002301611653002201634653002901656653002701685100002001712700001901732700002201751700002801773856008501801       2015                          d00aEnergy retrofit analysis toolkit for commercial buildings: A review0 aEnergy retrofit analysis toolkit for commercial buildings A revi  bElsevier Ltd.c09/2015  a1087-11000 v893 a<p>Retrofit analysis toolkits can be used to optimize energy or cost savings from retrofit strategies, accelerating the adoption of ECMs (energy conservation measures) in buildings. This paper provides an up-todate review of the features and capabilities of 18 energy retrofit toolkits, including ECMs and the calculation engines. The fidelity of the calculation techniques, a driving component of retrofit toolkits, were evaluated. An evaluation of the issues that hinder effective retrofit analysis in terms of accessibility, usability, data requirement, and the application of efficiency measures, provides valuable insights into advancing the field forward. Following this review the general concepts were determined: (1) toolkits developed primarily in the private sector use empirically data-driven methods or benchmarking to provide ease of use, (2) almost all of the toolkits which used EnergyPlus or DOE-2 were freely accessible, but suffered from complexity, longer data input and simulation run time, (3) in general, there appeared to be a fine line between having too much detail resulting in a long analysis time or too little detail which sacrificed modeling fidelity. These insights provide an opportunity to enhance the design and development of existing and new retrofit toolkits in the future.</p>10aBuilding energy retroﬁt10aEnergy conservation measures10aEnergy efﬁciency10aEnergy simulation10aRetroﬁt analysis tools10aWeb-based applications1 aLee, Sang, Hoon1 aHong, Tianzhen1 aPiette, Mary, Ann1 aTaylor-Lange, Sarah, C.  uhttps://simulationresearch.lbl.gov/publications/energy-retrofit-analysis-toolkit