Comparative analysis of simulation tools for developing, testing, and benchmarking advanced control algorithms in building energy management systems

Buildings are an important part of the energy consumption of cities. With recent developments in integrated energy systems in buildings, the need for a smart energy management system (EMS) has significantly increased. In this regard, AI-EMS can help to enhance operational efficiency, occupant comfort, and environmental sustainability in urban areas. However, a comprehensive framework categorizing the tools and algorithms used in buildings and urban EMS is still lacking, which limits the ability to evaluate the effectiveness of these technologies. This paper addresses this gap by analyzing and comparing some of the most widely used AI tools, algorithms, and simulation environments for optimizing building energy systems, offering insights into the applications, strengths, and limitations of each tool. We provide a structured overview of AI control methods and available EMS tools, as well as a comparative analysis of their capabilities for energy management in both individual buildings and district-level systems. We aim to help researchers, policymakers, building designers, and engineers to better understand the available simulation tools for making informed decisions when selecting and using them.