A distributed multilabel classification approach towards mining appliance usage in smart homes

Demand side management of Energy in smart buildings often needs as a prerequisite (or benefits from) future appliance usage schedules of the occupants. In this work, a datadriven appliance usage prediction framework is proposed. The dependence of consumption trends on recent appliance usage history is explored through the use of temporally sensitive feature representations. Multi-label classification architectures, with appliance level adaptive thresholding are used as the predictive models. A data parallel distributed architecture is explored for our machine learning models to feasibly analyze larger data volumes generated by a Smart Building. The framework is extensively tested on the publicly available GREEND dataset. Appliance usage patterns are obtained at multiple temporal resolutions with encouraging results with lower training times demonstrated by our models on the distributed architecture.