This research project investigated how energy consumption could be reduced in an existing research building at the Research Center for Environment and Sustainable Development in Tehran. The study examined the effects of building characteristics and operational conditions to support decisions about potential energy improvements.
The research team combined EnergyPlus simulation, artificial neural networks, and a genetic algorithm to predict energy use and evaluate alternative parameter combinations. The analysis identified occupancy and external-wall thermal performance among the most influential factors affecting energy consumption.
The optimised scenario indicated a potential reduction of approximately 35% in energy consumption compared with the study baseline. The result demonstrates the value of simulation and AI-assisted optimisation in assessing retrofit options before implementation.
