# Machine Learning in Smart Buildings: A Review of Methods, Challenges, and Future Trends
## 1 Introduction  
Smart buildings represent integrated cyber-physical systems, designed to coordinate and control various essential services such as Heating, Ventilation, and Air Conditioning (HVAC), lighting, life-safety systems, and other ancillary services through advanced automation and supervisory mechanisms. These intelligent infrastructures are supported by interoperable communication protocols, which facilitate seamless cross-domain data exchange. This capability ensures that events detected within one subsystem can trigger compensatory actions in other interconnected systems. To achieve this, heterogeneous sensor streams are processed by real-time analytics engines that infer crucial information, such as occupancy states and environmental conditions. This data is then utilized to continuously update control policies online through adaptive learning algorithms. The ultimate objective of this architectural design is to achieve occupant-centric operation, therebypreserving thermal and visual comfort while simultaneously safeguarding health. Concurrently, these systems strive to minimize energy consumption and reduce associated greenhouse gas emissions [1]. The urgency of improving energy efficiency in buildings is underscored by the fact that the global building sector is a significant contributor to environmental impact, accounting for approximately 40% of total energy consumption and 36% of global CO\({}_{2}\) emissions [2]. The integration of ML into building management systems marks a significant and transformative advancement aimed at addressing these pressing energy challenges. ML algorithms possess the inherent capability to process vast quantities of data originating from diverse building systems, including HVAC, lighting, and occupancy sensors. This processing enables them to identify intricate patterns, accurately predict future states, and optimize operational parameters in real time. This data-driven approach signifies a fundamental shift from static, rule-based control mechanisms to dynamic, adaptive management strategies that can intelligently respond to ever-changing conditions and occupant behaviors [3]. Unlike traditional building automation systems, which often struggle to adapt to unexpected changes or learn from historical patterns, ML-powered systems continuously learn and improve. This continuous learning capability provides superior predictive insights, allowing for the anticipation of equipment failures, the optimization of energy consumption based on real-time occupancy data, and the more effective integration of renewable energy sources [4]. ML applications in smart buildings span the entire lifecycle, from design optimization to operational management and retrofit planning. As illustrated in Figure 1, key areas where ML is impactful include the following:  
* Energy-efficient building design: ML models are employed to analyze various design elements, such as building geometry, orientation, and material properties. By predicting energy consumption patterns even before construction commences, these models enable the optimization of designs for minimal energy use [5]. Recent studies have shown that ML-based design optimization can lead to a reduction in predicted energy consumption by 15% to 25% when compared to conventional design approaches [6]. * Building automation: ML dynamically optimizes control strategies for critical building systems, including HVAC, lighting, and equipment scheduling. This optimization is based on real-time sensor data, allowing for highly responsive adjustments. Research has shown that ML-enhanced automation can reduce energy consumption by 10% to 30% while simultaneously improving occupant comfort levels [1,7]. * Fault detection and diagnosis (FDD): ML algorithms are instrumental in identifying anomalies that indicate equipment faults or degradation. Supervised learning models, for instance, can classify specific failure modes with accuracy rates often exceeding 90% [8].