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

Abstract

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 [1]. To achieve this target, more detailed data on the electricity consumed by each appliance are needed. Smart meters cannot meet this

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