Integrated, Artificial-Intelligence-Based Power Management for Building Electrical Microgrids

As global energy demand continues to rise alongside the push for green technologies, artificial intelligence (AI) based power management systems play a pivotal role in achieving energy efficiency, grid stability and carbon footprint reduction, making them a vital component of future-ready building infrastructures. In this paper, an integrated AI based method for power management of building electrical systems is proposed. The main goal is to develop an accurate model to estimate the indoor temperature of building thermal zones, which is a critical aspect of energy management and occupant comfort. To achieve this, advanced modelling techniques are applied, specifically system identification and artificial neural networks (ANNs). Moreover, a sophisticated approach to real-time building energy management through accurate estimation of internal thermal zone gains is suggested, by applying heuristic parameter estimation techniques. This problem involves using the proposed ANN building model in the process of internal thermal gains estimation for each building thermal zone. By developing and validating these models, the aim is the efficiency of building electrical systems to be enhanced, the energy consumption be reduced, and the thermal comfort within buildings be improved, contributing to more sustainable and cost-effective building power management methods.