Pattern recognition and classification for electrical energy use in residential buildings

In the last years, researchers and energy utilities are showing a rising interest in the study and definition of actual buildings' energy uses. A key aspect of this investigation is the description of daily energy use patterns and their variability over the time. This paper discusses the application of machine learning techniques for pattern recognition with the implementation of a Self-Organizing Map (SOM) algorithm coupled with a k-means clustering algorithm on a dataset of registered electrical energy use in a residential building located in Milan. In the study, five clusters emerged with different daily patterns, that can be ascribed to different uses of electric appliances by people inside the flats.