AI-Enabled Applications for the Built and Energy Environment

The built environment and the energy sector are undergoing a profound transformation. Buildings, districts, smart grids, microgrids, energy communities, and urban energy systems are becoming increasingly interconnected, creating cyber-physical ecosystems that require continuous monitoring, adaptive control, and coordinated decision making. This transition is supported by advances in smart sensing, digital technologies, and advanced control systems.Artificial intelligence is playing a vital role in this transition. Recent advances in machine learning, deep learning, model predictive control, and data-driven optimization are enabling smarter and more efficient operation of buildings and energy systems. Such technologies support accurate forecasting, real-time control, fault detection, autonomous energy management, demand response, renewable energy integration, and the coordinated operation of HVAC systems, photovoltaic installations, batteries, electric vehicles, heat pumps, and other flexible energy resources.The current Research Topic, “AI-Enabled Applications for the Built and Energy Environment”, welcomes original research that advances intelligent monitoring, control, optimization, and management across buildings and energy systems. We invite contributions ranging from methodological developments and review articles to simulation studies, experimental research,real-world demonstrations, and industrial case studies that support the development of more efficient, resilient, sustainable, and intelligent built environments.Topics of interest include, but are not limited to:▪ AI-enabled control and optimization of building energy systems▪ Smart control of HVAC systems, thermal comfort, indoor air quality, and energy efficiency▪ Reinforcement learning, deep reinforcement learning, and safe learning-based control forbuildings and energy systems▪ Multi-agent coordination of smart buildings, microgrids, energy communities, anddistributed energy resources▪ Model predictive control, adaptive control, hybrid control, and data-driven control▪ AI-based energy management for smart buildings, districts, campuses, and urban energysystems▪ AI-enabled forecasting, control, and optimization of renewable energy sources, includingphotovoltaic systems, wind energy systems, and hybrid RES-based energy infrastructures;▪ Demand response, energy flexibility, and grid-interactive efficient buildings▪ AI applications for renewable energy integration, photovoltaic systems, batteries, electricvehicles, and heat pumps▪ Digital twins and cyber-physical systems for energy monitoring, simulation, control, andoptimization▪ Machine learning for energy forecasting, load prediction, occupancy estimation, andsystem diagnostics▪ Edge AI, IoT, smart sensing, and real-time decision making▪ Intelligent energy management strategies for integrating renewable energy sources withbuildings, microgrids, energy communities, storage systems, electric vehicles, and flexibleloads;▪ AI-enabled optimization for energy communities, peer-to-peer energy trading, collectiveself-consumption, and local energy markets▪ Explainable, trustworthy, resilient, and human-centric AI for buildings and energy systems▪ Review articles covering recent advances in AI for building energy management, smartgrids, microgrids, energy communities, and sustainable urban infrastructureWe invite researchers, engineers, practitioners, and industry experts to contribute high-quality research that advances the application of artificial intelligence across the built and energy environment. By bringing together innovations in AI, energy systems, and smart infrastructure, this Research Topic aims to promote solutions that support a more efficient, resilient, and sustainable energy future. Topic editors: Panagiotis Michailidis, Federico Minelli, Hasan Huseyin COBAN Source: https://www.frontiersin.org/research-topics/82676/ai-enabled-applications-for-the-built-and-energy-environment