A Case Study to Quantify Variability in Building Load Profiles

Abstract

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 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' 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.

A Framework to Predict Variability Characteristics in Building Load Profiles

“…The proposed framework in this research is part of ongoing work and it is divided into two phases of discovery and implementation (shown in Figure 2). The discovery phase begins with a decomposition process previously discussed in [1] and [26], applied to a subset of available high-resolution measured data, to separate the variability signal from the base load. Next, the variability signal is analyzed and characterized by various metrics such as root-mean-square-variability (RMSV) [1] that summarize the typical deviation of the base load over a given period of time, or by other metrics that are related to its statistical distribution.…”

“…The discovery phase begins with a decomposition process previously discussed in [1] and [26], applied to a subset of available high-resolution measured data, to separate the variability signal from the base load. Next, the variability signal is analyzed and characterized by various metrics such as root-mean-square-variability (RMSV) [1] that summarize the typical deviation of the base load over a given period of time, or by other metrics that are related to its statistical distribution. This represents an important distinction from other types of high-resolution analysis, in that the variability is not directly modeled as a time-series signal, but rather characterized by these scalar metrics.…”

“…The first step in developing a model that relates highresolution load profile characteristics to low-resolution characteristics is to separate these two components of the signal. Previous research by the authors in [1] proposes the use of the Discrete Wavelet Transform (DWT). The DWT process separates a signal into an approximation signal and a detail signal, corresponding to the base load and variability, respectively.…”

“…With an increasing number of connected distributed energy resources (DERs) in the grid, and electrical loads in the built environment, the real-time impact on voltage, frequency, and power generation/consumption should be evaluated [1].…”

Innovative Approaches to Smart Energy Integration in Core Electrical Systems

“…It is clear from these studies that adding smart energy can make electricity grids more efficient, reliable, and long-lasting.Advanced control systems for handling the addition of green energy sources to current power lines are an important area of study. For instance, [7] suggested a hierarchical control scheme for organizing the operation of spread energy resources like wind mills and solar panels in order to get the most energy out of the energy that is produced and used. To keep the grid stable and reliable, the system uses both local and centralized control methods.Storage systems for energy are an important part of smart energy integration because they let us save extra energy from green sources for times when demand is high.…”