Clustering Based Energy Management of Smart Buildings /

Abstract

Developing countries have witnessed a remarkable surge in energy crisis due to the supply and demand gap. One of the solutions to overcome this problem is the optimal use of energy that can be achieved by employing demand-side management (DSM) and demand response (DR) methods intelligently. Machine learning and data analysis tools help us make intelligent systems that motivate us to use machine learning to implement DSM/DR programs. Our buildings are getting smarter because of smart end-use devices, integration of communication infrastructure with smart controllers, energy storage systems (ESS), and integration of renewable energy resources (RER). In this work, a novel DSM algorithm is introduced to implement DSM intelligently by using artificial intelligence. Data of residential devices is collected by using smart meters to generate the patterns and train the machine for pattern recognition. A comparison of training results between decision tree and ANN shows the superiority of ANN in pattern recognition. The results showed an efficient implementation of ANN along with demand-side management whereas the peak and off-peak loads are normalized to a certain range where a perfect agreement between supply and demand can be reached