Classifying Appliances Operation Modes Using Dynamic Time Warping (DTW) And K Nearest Neighbors (KNN)
Abstract
In the Smart Grid environment, the advent of intelligent measuring devices facilitates monitoring appliance electricity consumption. This data can be used in applying Demand Response (DR) in residential houses through data analytics, and developing data mining techniques. In this research, we introduce a smart system approach that is applied to user's disaggregated power consumption data. This system encourages the users to apply DR by changing their behaviour of using heavier operation modes to lighter modes, and by encouraging users to shift their usages to off-peak hours. First, we apply Cross Correlation to detect times of the occurrences when an appliance is being used. We then use two approaches to recognize the operation mode used: The Dynamic Time Warping (DTW), and Machine Learning using K-Means and K-Nearest Neighbors (KNN).