Efficient energy consumption in smart buildings using personalized NILM-based recommender system

As the construction sector accounts for the highest energy consumption worldwide, new solutions must be offered in buildings through the adoption of energy-efficient techniques. The main factors involved in energy consumption and residents' behaviors patterns considering environmentally-friendly lifestyle changes must be clearly identified and modeled to provide such solutions. One of the most important topics in smart grids is managing energy consumption in buildings, and one way to optimize energy consumption by analyzing building energy data is to use personalized recommender systems. The Non-Intrusive Load Monitoring (NILM) technique is an important way to cost-effective real-time monitoring the energy consumption and time of use for each appliance. However, the combination of recommender systems and NILM has received less attention. In this paper, a personalized NILM-based recommender system is proposed, which has three main phases: DAE-based NILM, TF-IDF-based text classification, and personalized recommender system. The proposed approach is investigated using the Reference Energy Disaggregation Dataset (REDD). According to the results, the accuracy of the proposed framework is about ۶۰%.

Keywords:

Smart buildings , Recommender systems , NILM , Deep Learning , TF-IDF