A cross-sectional study of the temporal evolution of electricity consumption of six commercial buildings

Abstract

Current approaches to building efficiency diagnoses include conventional energy audit techniques that can be expensive and time consuming. In contrast, virtual energy audits of readily available 15-minute-interval building electricity consumption are being explored to provide quick, inexpensive, and useful insights into building operation characteristics. A cross sectional analysis of six buildings in two different climate zones provides methods for data cleaning, population-based building comparisons, and relationships (correlations) of weather and electricity consumption. Data cleaning methods have been developed to categorize and appropriately filter or correct anomalous data including outliers, missing data, and erroneous values (resulting in < 0.5% anomalies). The utility of a cross-sectional analysis of a sample set of building's electricity consumption is found through comparisons of baseload, daily consumption variance, and energy use intensity. Correlations of weather and electricity consumption 15-minute interval datasets show important relationships for the heating and cooling seasons using computed correlations of a Time-Specific-AveragedOrdered Variable (exterior temperature) and corresponding averaged variables (electricity consumption)(TSAOV method). The TSAOV method is unique as it introduces time of day as a third variable while also minimizing randomness in both correlated variables through averaging. This study found that many of the pair-wise linear correlation analyses lacked strong relationships, prompting the development of the new TSAOV method to uncover the causal relationship between electricity and weather. We conclude that a combination of varied HVAC system operations, building thermal mass, plug load use, and building set point temperatures are likely responsible for the poor correlations in the prior studies, while the correlation of time-specific-averaged-ordered temperature and corresponding averaged variables method developed herein adequately accounts for these issues and enables discovery of strong linear pair-wise correlation R values. TSAOV correlations lay the foundation

Impacts of access to and use of electricity on households’ economic status in selected informal settlement areas of <i>Woreda 12</i>, Yeka Sub City, Addis Ababa, Ethiopia

“…The dependent variables in the regression models are households' family income and total energy expenditures. It captures the latent benefit and the expected desirability of electric use by applying the model as follows (Pickering, et al, 2017;Wohlfarth, Eichhammer, Schlomann, & Worrell, 2018):…”

“…The ESR model introduces exogenous variables affecting family income. It helps to avoid errors in estimating ATE simply by introducing instrumental variables as presented in Table 7 (Ifegbesana, Rampedia, & Annegarn, 2016;Pickering, et al, 2017;Wohlfarth, Eichhammer, Schlomann, & Worrell, 2018). The reverse effect of family income on the independent variables in turn induces changes in the explanatory variables and the final outcome of the model.…”

“…create home-based businesses, increase household income, and reduce environmental damages (Bhattacharjee & Reichard, 2011;Pickering, et al, 2017;Li, et al, 2019). Most importantly, it saves households' fuel expenditures and helps to enjoy a wider range of energy services to domestic activities (Lloyd, 2017;Hanania, Stenhouse, & Donev, 2018).…”

Analysis of Methodology for Scaling up Building Retrofits: Is There a Role for Virtual Energy Audits?—A First Step in Hawai’i, USA

“…These functions may include baseload to peak energy ratio, energy pattern snapshot, HVAC schedule (on/off times), and building occupancy patterns. When a small population of buildings of different categories and climates are analyzed in a virtual energy audit, varying usage characteristics such as baseload, daily operation, and energy use intensity (EUI) may be clearly identified [12].…”

Automated pipeline framework for processing of large-scale building energy time series data

“…Due to the cost and time required for conventional on-site building energy audits, "virtual" energy audits, which has no need for setting foot in a building and use appropriate diagnostic and prognostic tools, is an important goal of building research. We have developed a virtual energy audits tool, EDIFES (Energy Diagnostics Investigator for Efficiency Savings) using a data-driven analytical approach based on smart-meter data provided by electrical utility companies or building owners [4][5][6][7][8].…”