Single Appliance Recognition Using Statistical Features Based K-Nn Classification

Recognizing the appliance according to the flowed electric current through it is quite a meaningful work which can help the electric management system to make effective policy of energy conservation. We designed an algorithm based on an improved k-nearest neighbor which can classify the unlabelled appliances' running power data into its most similar data clusters. In other words, this algorithm is able to recognize the appliance only according to its running power data series. The classification is based upon the multifarious features extracted from the time series data sensed from the running appliance with the power metering sensors. Appliance recognition is performed with a mean accuracy over 90% in five-class classification problem.