Real Time Identification of Electrical Devices through Power Consumption Pattern Detection Vibhatha Abeykoon, Nishadi Kankanamdurage, Anuruddha Senevirathna, Pasika Ranaweera, Rajitha Udawalpola Dept. of Electrical and Information Engineering, University of Ruhuna, Galle, Sri Lanka Email: {vibhatha; kd.nishadi1;anu19910318}@gmail.com, {pasika; rajitha}@eie.ruh.ac.lk Abstract — This research discusses a way to identify electrical devices in real time using intelligent techniques through data analysis. The electrical device identification process is initiated by collecting information related to power consumption of electrical appliances which are used in domestic life. A prototype data acquisition system was implemented to extract parameters such as active power, reactive power, phase shift, root mean square voltage and current from the appliances connected to it. The analysis is done using neural networks, support vector machines, k-means, mean-shift and silhouette classifiers. The purpose of this study is to select the best classifier which produces the optimum results in detecting and identifying electrical appliances in real time from their electric parameters. The selected classifier is used to determine a power consumption pattern (signature) for different electric appliances. Keywords - classification, clustering, k-means, mean-shift, neural networks, silhouette, support vector machines I. INTRODUCTION As an intelligent approach, machine learning techniques can be used to understand the meaning of a data set in a logical way and provide useful outputs from raw data for different purposes. In this research, a few supervised and unsupervised learning methods are compared with a constant data set and a better classifier is chosen for the data clustering and prediction. In considering power consumption patterns, neural networks and support vector machines were used as supervised learning methods to classify data and predict patterns. Basically, the real time electrical device identification is done by comparing the power consumption features of each device with the other devices and clustering the data sets in the training period and predicting the electrical device connected to the system with a new data set. Here the main variables considered in this research are active power, reactive power, phase shift, root mean square voltage and current. The data collection is done covering all the modes of operations and all the statuses of each electrical device in order to get a fully understanding about the behavior of their functionality. The purpose is to train the system to identify the electrical device in any moment of their cycle of functioning. The challenging factor that was seen in the research is to understand and collect data for the complete cycle containing all the statuses acquired by the electrical device. In the realization of the actions of a particular device, data has to be collected covering all the scenarios as far as the performance of a particular device is considered. II. RELATED WORK A device called smart plug is created to detect the power consumption from each device. And the smart plug identifies each device using machine learning techniques and classifies data for further analysis [2], [3], [1]. There are many researches done to detect electrical devices in real time and some different researches were done to optimize and predict the power consumption [4], [5], [6], [7]. Here both these scenarios are addressed in order to provide an advanced overview on power consumption at domestic level in Sri Lanka. There are researches done to detect electrical devices in real time to collect data with better classification in the earlier stages of data acquisition to provide a solid foundation for data analysis purposes [14]. Later on neural networks and classification algorithms are used to detect consumption patterns and identify and cluster the electrical devices in purpose of real time device identification. In the electrical device identification domain, the related researches were more focused on extracting data from many devices and classifying them [16]. In understanding and classifying the electrical devices, there are limited numbers of features that can support the task [17]. Here the main attention was paid to the active power consumption. When it comes to devices which are consuming similar amount of power, this factor is not enough to classify the devices. In this case more features were considered to support the classification task [20]. These features are reactive power, phase shift, root mean square voltage and current.  Support Vector Machines.  Artificial Neural Networks.  K-Means Classification.  Silhouette Classification.  Mean-Shift Classification In considering the machine learning algorithms used in the research, the reason for choosing a number of algorithms is that the way these algorithms converge to a result is different from each other as far as the research objective is considered. The support vector machines were not used in most of the researches done in the electrical device identification. Most of the time the artificial neural networks [9] were used to perform the clustering and identification tasks. Support vector machine algorithm [8], [18], [19] was 2016 First International Conference on Micro and Nano Technologies, Modelling and Simulation 978-1-5090-2406-3/16 $31.00 © 2016 IEEE DOI 10.1109/MNTMSim.2016.13 11 2016 First International Conference on Micro and Nano Technologies, Modelling and Simulation 978-1-5090-2406-3/16 $31.00 © 2016 IEEE DOI 10.1109/MNTMSim.2016.13 11