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Appliance Load Monitoring is a technique for monitoring and obtaining detailed information about devices in homes or industry. Acquisition of appliance-level data can provide benefits in many areas such as energy management, demand response and load forecasting. However, monitoring process is often provided with a costly installation, as it requires a large number of sensors and a data center. In contrast, Non-Intrusive Load Monitoring (NILM) is proposed as alternative cost-efficient load monitoring solution. Simply put, NILM is the process of obtaining appliance-level data by analyzing the data read from the main meter that measures the electricity consumption of the whole house. Before NILM analysis is performed, the load patterns of the appliances are usually modeled individually. In general, one model for each appliance is modeled even if the appliance has more than one operating program such as washing machine. Therefore, when the appliance operates in other programs, the NILM analysis accuracy decreases. In this paper, an appliance-based NILM analysis has been made considering the appliances having multiple operating programs. In order to increase the success of NILM analysis, Deep Learning method which is the most important data-driven technique of recent times is used. Developed DL models were tested in IoT Microgrid Laboratory environment.
Konya Technical University, Turkey
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