2 research outputs found
ALDI++: Automatic and parameter-less discord and outlier detection for building energy load profiles
Data-driven building energy prediction is an integral part of the process for
measurement and verification, building benchmarking, and building-to-grid
interaction. The ASHRAE Great Energy Predictor III (GEPIII) machine learning
competition used an extensive meter data set to crowdsource the most accurate
machine learning workflow for whole building energy prediction. A significant
component of the winning solutions was the pre-processing phase to remove
anomalous training data. Contemporary pre-processing methods focus on filtering
statistical threshold values or deep learning methods requiring training data
and multiple hyper-parameters. A recent method named ALDI (Automated Load
profile Discord Identification) managed to identify these discords using matrix
profile, but the technique still requires user-defined parameters. We develop
ALDI++, a method based on the previous work that bypasses user-defined
parameters and takes advantage of discord similarity. We evaluate ALDI++
against a statistical threshold, variational auto-encoder, and the original
ALDI as baselines in classifying discords and energy forecasting scenarios. Our
results demonstrate that while the classification performance improvement over
the original method is marginal, ALDI++ helps achieve the best forecasting
error improving 6% over the winning's team approach with six times less
computation time.Comment: 10 pages, 5 figures, 3 table
ALDI plus plus : Automatic and parameter-less discord and outlier detection for building energy load profiles
10.1016/j.enbuild.2022.112096ENERGY AND BUILDINGS26510.1016/j.enbuild.2022.11209