Optimized Building Energy Management System of a Residential Green Building

Managing energy in residential buildings presents significant challenges, requiring effective and reliable oversight of electricity consumption to alleviate pressure on the power grid. Energy consumption in buildings is projected to increase by over 50% within the next two decades. Electricity continues to be the predominant energy source utilized by buildings, and this demand is on the rise. To address the effects of this increasing demand, it is essential to implement strategies aimed at enhancing the energy efficiency of buildings. In residential structures, the rise in energy consumption is attributed to home appliances and various other equipments. Building energy management systems (BEMS) assist occupants in enhancing energy efficiency, thereby enabling residential buildings to reduce their energy usage. This paper focuses on modelling and regulating the main electrical appliances to address the issue of energy consumption management in residential green building (RGB). Solar photovoltaic (PV), biomass, electric vehicle (EV), and battery energy storage system (BESS) integrated with grid are considered to provide electrical power to the RGB. The presented model analyses the energy consumption of the residential load and assists in determining the contribution of each component to the peak demand. Initially, the constrained optimization problem is mathematically formulated, then it is solved through particle swarm optimization (PSO) algorithm, genetic algorithm (GA), and artificial bee colony (ABC) algorithm and finally the results of these optimization techniques are compared.