Appliances Identification for Different Electrical Signatures using Moving Average as Data Preparation

Abstract

Intelligent electronic equipment and automation network are the brain of high-technology energy management systems in the critical role of smart homes dominance. The smart home is a technology integration for greater comfort, autonomy, reduced cost as well as energy saving. In this paper, a system which can automatically recognize home appliances and based on a dataset of electric consumption profiles is proposed. The dataset ACS-F1 (Appliance Consumption Signature Fribourg 1) available online and containing 100 appliances signatures in XML (Extensible Markup Language) format is used for that purpose. A new format for this dataset is created as it makes easier to implement directly machine learning algorithm such as K-NN (K-Nearest Neighbors), Random Forest and Multilayer Perceptron in the feature space between the test object and the training examples. In order to optimize the classification algorithm accuracy, we propose to use a moving average function for reducing the random variations in the observations. Using this technique indeed allows the structure of the underlying causal processes to be better exposed. Moving average is widely used in trading algorithm to predict the future price movements based on identifying patterns in prices, volume and other market statistics. Recognition results using K-NN based machine learning are provided to show the impact of the number and the type of electrical signatures. In the best case an accuracy rate of 89.1% and 99.1% is obtained using K-NN, without and with moving average respectively. Our approach is compared with another data preparation technique based on dynamical coefficient and used to optimize the K-NN classifier as well. Finally, our approach based on moving average is also evaluated with Random Forest (99%) and Multilayer Perceptron (98.8%) classification algorithms for the best electrical signature obtained with K-NN.