Load-Based Classification of Academic Buildings using Matrix Profile and Supervised Learning
Cristina Nichiforov, Miltiadis Alamaniotis · 2021 IEEE PES Innovative Smart Grid Technologies Europe (ISGT Europe) · 2021
The deployment and operation of advanced monitoring infrastructure in non-residential buildings have accommodated the acquisition of datasets in the form of long multivariate time series pertained to energy use. These datasets may be used to extract information in order to characterize and improve the building's operation by establishing energy consumption patterns, identifying abnormal operating modes, and detecting and anticipating faults ahead of time. The current research focuses on a two-fold data analysis method applied to academic building energy data. Initially, Matrix Profile (MP) is used for feature extraction - i.e., MP is a time-series data mining technique - to extract and build feature datasets of anomaly patterns from academic building energy traces. Second, the extracted feature datasets are fed to two supervised machine learning classification models aiming at discriminating the type of building load pattern. The case study is carried out on a set of public real-world energy load consumption patterns of four different types of academic buildings. The method can prove useful for exploiting complementary load patterns in a control infrastructure attaining efficient building energy management and reliable operation.