Applied Mathematics & Information Sciences

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Efficient energy consumption has always been of significant interest to decision-makers in many countries. Awareness, knowledge and a real understanding of proper use of energy patterns is a key element in improving consumption behaviour. Despite the amount of available knowledge on how to save energy, many consumers still fail to take noticeable steps to enhance energy efficiency and conservation. Many significant and innovative studies have been conducted, yet there is still room for more sophisticated approaches to persuade users to optimize energy consumption. Therefore, integrating the Internet-of-Things (IoT) devices such as smart meters and mobile applications in a coherent framework would be one solution to achieving the desired changes in energy consumption behaviour. The present paper investigates current work in progress for optimizing energy use with IoT devices to provide sufficient feedback for users. This paper adopts a non-intrusive load monitoring algorithm (NILM) to assist in generating a recommender system based on smart meter data. The NILM identifies appliances and patterns of user consumption behaviour and disaggregates consumption of individual appliances from a single-point smart meter data. The results benefits not only household consumers but also energy providers and top decision-makers.

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