Turkish Journal of Electrical Engineering and Computer Sciences




Energy-saving and efficiency are as important as benefiting from new energy sources to supply increasing energy demand globally. Energy demand and resources for energy saving should be managed effectively. Therefore, electrical loads need to be monitored and controlled. Demand-side energy management plays a vital role in achieving this objective. Energy management systems schedule an optimal operation program for these loads by obtaining more accurate and precise residential and commercial loads information. Different intellegent measurement applications and machine learning algorithms have been proposed for the measurement and control of electrical devices/loads used in buildings. Of these, nonintrusive load monitoring (NILM) is widely used to monitor loads and gather precise information about devices without affecting consumers. NILM is a load monitoring method that uses a total power or current signal taken from a single point in residential and commercial buildings. Therefore, its installation and maintenance costs are low compared to other load monitoring methods. This method consists of signal processing and machine learning processes such as event detection (optional), feature extraction and device identification after the total power or current signal is acquired. Up to now, many techniques have been proposed for each processes in the literature. In this paper, techniques used in NILM systems are classified and a comprehensive review is presented.


Energy management, signal processing, event detection, feature extraction, machine learning

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