Smart-Building Applications: Deep Learning-Based, Real-Time Load Monitoring

Google's Director of Research, Peter Norvig said that “We don’t have better algorithms than anyone else, we just have more data”. This inspiring statement shows that having more data is directly related to better decision making and having the foresight about the future. With the development of the Internet of Things (IoT) technology, it is now much easier to gather data. Technological tools such as social media websites, smartphones, and security cameras can be considered as “data generators”. When the focus is shifted to the energy field, these generators are “Smart Meters”. Smart meter technology incorporates many intelligent functions and offers great opportunities for utility operators, prosumers, and consumers. Although smart meters are referred to as ‘smart’, they might not be intelligent enough depending on the final purpose. Meter data generally provide more benefits for the utility side than for the consumer side. However, with the smart meter data, customers can be offered great opportunities, where they may be able to make more conscious decisions. Previous studies have reported that if instantaneous energy consumption data are given to the consumers as feedback, approximately 20% of energy savings can be achieved per household.

This article introduces the NILM method, which can contribute to energy management and savings in residential, industrial, and naval uses. Up-to-date data-driven NILM solutions and advantages of DL-based analysis are explained in detail. Also, a multi-label DL approach, which can save training time and reduce the need for model storage, is presented and tested in real-time. Considering that the studies in the literature are carried out offline, the online analysiscapacity of recent DL models has been tested in a laboratory environment. In this way, the accuracy difference between offline and online implementations has been revealed.