A Case Study to Quantify Variability in Building Load Profiles

A Case Study to Quantify Variability in Building Load Profiles

Blog Article

Recent technology development and penetration of advanced metering infrastructure (AMI), advanced building control systems, and the internet-of-things (IoT) in the built environment are providing detailed information on building operation, performance, and user’s comfort and behavior.Building owners can obtain a wide range of energy consumption details at various levels of time granularity to augment their decisions as they manage the building operation and interact with the grid.AMI data are providing a lick em sticks candy new level of detail and visibility that may enhance building services and assets in the smart grid domain and make buildings inch closer to becoming a grid-interactive energy efficient buildings (GEB).While utility-installed AMI typically records energy consumption at a 15, 30, or 60-minute resolution, building-owner-installed metering can record energy consumption at one-minute or sub-minute time scales, providing information about how much the energy consumption varies from one sub-minute to the other (i.

e.variability) at a finer time resolutions than typically available from AMI.This paper examines one-minute building load profile data sets and presents a framework to study, define, extract, quantify and analyze variability in buildings’ ds durga hand soap load profiles.The discussion of variability and its analysis is based on a case study of an actual sub-minute time-resolution data set, collected in 2019, for two buildings in a Midwest state in the USA.

The result shows that for the case studies, the level of variability in an end-use category is not simply proportional to its consumption.Furthermore, distinct and predictable daily variability patterns emerge in end-use load categories.This information is useful for a host of applications including prediction, forecasting, and modeling.