Islands of misfit buildings: Detecting uncharacteristic electricity use behavior using load shape clustering

Many energy performance analysis methodologies assign buildings a descriptive label that represents their main activity,often known as the primary space usage(PSU).This attribute comes from the intent of the design team based on assumptions of how the majority of the spaces in the building will be used.In reality,the way a building’s occupants use the spaces can be different than what was intended.With the recent growth of hourly electricity meter data from the built environment,there is the opportunity to create unsupervised methods to analyze electricity consumption behavior to understand whether the PSU assigned is accurate.Misclassification or oversimplification of the use of the building is possible using these labels when applied to simulation inputs or benchmarking processes.To work towards accurate characterization of a building’s utilization,we propose a modular methodology for identifying potentially mislabeled buildings using distance-based clustering analysis based on hourly electricity consumption data.This method seeks to segment buildings according to their daily behavior and predict which ones are misfits according to their assigned PSU label.This process finds potentially uncharacteristic behavior that could be an indication of mixed-use or a misclassified PSU.Our results on two public data sets,from the Building Data Genome(BDG)Project and Washington DC(DGS),with 507 and 322 buildings respectively,show that 26%and 33%of these buildings are potentially mislabelled based on their load shape behavior.Such information provides a more realistic insight into their true consumption characteristics,enabling more accurate simulation scenarios.Applications of this process and a discussion of limitations and reproducibility are included.

AI-Assisted approach for building energy and carbon footprint modeling

This paper proposes an energy and carbon footprint modelling using artificial intelligence technique to assess the impact of occupant density for various types of office building.We use EnergyPlus to simulate energy con-sumption,and then estimate the related CO₂emissions based on three years(2016–2018)of Actual Meteoro-logical Year(AMY)weather data.Various occupant densities were used to evaluate the annual energy consumption and CO₂emission.In this work,a robust deep learning technique of long short-term memory(LSTM)model was established to predict the time-series energy consumption and CO₂emissions.A power exponential curve was suggested to correlate the behaviour of annual energy and CO₂emission for occupant densities range from 10 to 100 m2/person for each office building type.The results of LSTM model show high prediction performance and small variations within the three types of office building data,which can be applied to the similar building model to predict and optimise energy consumption and CO₂emission.