Abstract
The electricity consumption demand increases every year. This is partly due to economic growth and the coming of new trend digital electric equipment including electric vehicle. In order to sustain electricity and reduce environment impact in the future, a method of electric energy conservation is necessary. To address this issue, a non-intrusive load monitoring (NILM), which can show load profile of each electric equipment from total electricity consumption load is presented in this paper. The electricity information of each equipment can help to manage electrical system supply and demand in view of electricity supplier and user. In this research, a supervised nonintrusive load monitoring algorithm is proposed to develop smart meter load profiles. This algorithm is tested through data collected from 10 types of equipment while some equipment has the same power level. The time used in training algorithm is less than NILM that is based on deep learning algorithm. In addition, a smart meter load profile is developed. Results are evaluated by accuracy of prediction load profile for monitoring of 10 types of equipment. The accuracy score of smart meter system was 1. 0 and the system can identify load profiles of equipment that have nearly identical power level. The performance of smart meter load profile in this research is evaluated based on assumption that none of electrical equipment state is changed from OFF to ON at the same time and vice versa. The result of this research is expected to improve smart grid technology and to help sustain electricity supply in the future.
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