Application of selected supervised learning methods for time series classification in Building Automation and Control Systems

摘要

Acquiring knowledge from the growing amount of Building Automation and Control Systems (BACS) data is becoming a more and more challenging and complex engineering task. However, it is also a prerequisite for smart and sustainable energy management as well as improving energy efficiency and comfort of building users. This report analyses the prospects of applying selected supervised learning methods for time series classification in BACS. Our training and testing data covered multivariate time series from 5,142 data points located in E.ON Energy Research Center building, describing observations from 22 classes, such as temperatures of gaseous fluid, C02 concentrations, heat flows, and operating messages. We trained thirteen types of classifiers: complex tree, medium tree, simple tree, linear Support Vector Machines, quadratic Support Vector Machines, boosted trees, bagged trees, subspace discriminant, subspace KNN, RUSBoosted Trees, Fine KNN, Coarse KNN and random forests. The highest demonstrated average classification accuracy concerned bagged trees (56.76%), with the maximum accuracy level of 76.54%. However, the maximum accuracy achieved by random forests was even higher, reaching 78.95%. Finally, we identified factors that may have a substantial influence on performance of particular methods.