Artificial Intelligence for Building Energy Management and Forecasting
A Special Issue of Energies (ISSN 1996-1073) belonging to the section "G: Energy and Buildings".
Deadline for manuscript submissions: 10 November 2026 | Viewed by 271
Editors
Interests: applications of building energy conservation technology; building environment; big data analysis; artificial intelligence technology in the engineering field
Interests: operations management; asset management; industrial maintenance; health and safety management; data fusion; condition monitoring; fault diagnosis; learning from failures; energy management; vibration-based fault detection
Special Issue Information
Dear Colleagues,
Buildings consume ~40% of global final energy and generate nearly one-third of CO2 emissions. As urbanization accelerates and climate commitments intensify, transforming building operations through intelligence, efficiency, and grid integration has become urgent. Traditional rule-based energy management systems struggle with dynamic occupant behavior-driven energy consumption, weather variability, renewable intermittency, and complex multi-system interactions.
Artificial Intelligence—encompassing machine learning, deep learning, reinforcement learning, graph neural networks, hybrid physics–AI approaches, and emerging Large Language Models (LLMs) and multimodal foundation models—offers transformative potential. Coupled with IoT sensing, digital twins, and edge computing, AI enables proactive, adaptive, and human-centric energy strategies. This Special Issue convenes interdisciplinary research to advance AI-driven solutions that enhance building efficiency, grid resilience, and decarbonization pathways toward net-zero built environments. We invite you to submit contributions on topics including, but not limited to, the following:
- Intelligent Forecasting:
- Short/long-term load forecasting (energy, thermal) with uncertainty quantification;
- Hybrid physics-informed ML models integrating building physics and data-driven learning for building energy management systems and operations;
- Transfer learning for cross-climate or cross-building generalization for building energy management systems and operations.
- Adaptive Control and Optimization:
- Reinforcement learning/MPC for HVAC, lighting, and shading control under comfort constraints;
- Multi-objective optimization balancing energy, cost, and emissions.
- Resilience and Diagnostics:
- AI-powered fault detection, diagnostics, and predictive maintenance (FDD) for building energy management systems;
- Anomaly detection for cybersecurity and operational resilience of building energy systems.
- Grid-Interactive and Sustainable Buildings:
- AI for demand response, virtual power plants, and building-to-grid (B2G) coordination;
- Optimization of on-site renewables, storage, and EV integration;
- Lifecycle assessment and AI-guided retrofit prioritization to promote net-zero buildings.
Dr. Qingyao Qiao
Dr. Akilu Yunusa-Kaltungo
Guest Editors
Manuscript Submission Information
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Keywords
- building energy management system
- energy efficiency
- artificial intelligence
- large language model
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