# Machine Learning in Smart Buildings: A Review of Methods, Challenges, and Future Trends
## 12 Conclusions  
This work explored the pivotal role of ML in enhancing the effectiveness and intelligence of smart buildings. It began by establishing a basic comprehension of smart buildings and the context of automated energy management systems. It then proceeded to analyze the complex elements and phases involved in using ML in building energy management systems. A comprehensive study was conducted to evaluate the significance of prediction metrics in accurately assessing energy efficiency. It analyzed several ML techniques, categorizing them into supervised, unsupervised, semi-supervised, and reinforcement learning methodologies. Each approach was further explored with its respective sub-categories and specific algorithms such as decision trees, SVM, ANNs. This classification not only highlighted the diverse range of ML applications in energy forecasting but also underscored the nuanced methods that address various aspects of energy management in smart buildings. The discussion on data management emphasized the vital stages of data collection, preprocessing, and feature extraction, which are imperative for the effective execution of ML. The study then examined the diverse applications of ML in energy forecasting, demonstrating its versatility in predicting energy demands over both short and long durations, as well as enhancing real-time energy utilization. An analysis of real-world case studies highlighted the actual applications and benefits of ML in smart buildings, offering a concrete understanding of its advantages and the enhancements it provides to energy efficiency. The accompanying examination of obstacles and possibilities provided insight into the tangible barriers, such as data privacy and infrastructure integration, while also emphasizing the potential for sophisticated predictive analytics in this field.  
Future research should prioritize developing areas such as predictive maintenance, renewable energy integration, and the convergence of machine learning with IoT technology. Specifically, it is essential to construct explainable machine learning frameworks to alleviate real-time cybersecurity threats while maintaining predictive accuracy. Reinforcement learning must also be optimized to harmonize occupant comfort with variable energy requirements within multi-objective limitations. Moreover, edge-based federated learning frameworks demonstrate potential for facilitating decentralized, privacy-preserving solutions that can more efficiently accommodate varied urban topologies compared to conventional centralized approaches. These research directions will likely drive the advancement of next-generation smart energy systems that are efficient, adaptive, transparent, secure, and aligned with human-centric design principles.  
Conceptualization, F.E.H., H.N.N., O.S. and K.C.; methodology, H.N.N. and K.C.; investigation, F.E.H. and H.N.N.; writing--original draft preparation, F.E.H. and K.C.; writing--review and editing, H.N.N., O.S. and K.C.; visualization, F.E.H.; supervision, H.N.N. and O.S.; project administration, H.N.N. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
Conflicts of Interest: Author [PERSON] is employed by the company DeepVu. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.