# Machine Learning in Smart Buildings: A Review of Methods, Challenges, and Future Trends
## 11 Future Research Directions  
Future ML-based IEMS research can concentrate on several important areas. By maximizing energy consumption, enhancing energy efficiency, and facilitating intelligent and sustainable energy management, these areas seek to improve the capabilities and efficacy of AIEMS further. Potential directions for future research include the following:  
* Sophisticated and advanced ML algorithms and models: They should be created, and they should be tuned specifically for AIEMS. To increase forecast accuracy, optimize control tactics, and facilitate improved energy management decision-making, this includes investigating deep learning techniques, reinforcement learning, generative adversarial networks (GANs), and other cutting-edge methodologies. * Integration of various data sources: Look into ways to use various data sources, like real-time energy, weather, occupancy, and building sensor data, to integrate and improve the accuracy and efficiency of AIEMS. This entails investigating data fusion strategies, data preprocessing techniques, and feature engineering techniques to glean insightful patterns and insights from various data streams. * Hybrid approaches and ensemble methods: Investigate the blending of various ML approaches, such as mixing supervised and unsupervised learning techniques or physics-based models with data-driven methodologies. Utilizing the advantages of various algorithms and models could result in more reliable and precise AIEMS solutions. * Scalability: Maintain AIEMS technologies that can handle big energy systems and adjust to changing conditions that are scalable. To achieve this, algorithms and structures that can quickly process and analyze vast amounts of data in real time, while also adapting to changing patterns of energy use, user behavior, and building characteristics, need to be developed. * Sustainability Considerations: As ML models, deep learning, and reinforcement learning frameworks require more computing power across various building types and climates, scalability issues arise. Moreover, frequent retraining may be necessary to accommodate evolving building dynamics and external variables, which can lead to increased energy consumption. Future research is needed to explore efficient algorithms, data management strategies, and hardware optimizations to reduce the computational costs of these systems, ensuring genuine sustainability. Furthermore, it is crucial to create scalable frameworks that preserve energy savings while minimizing environmental costs to attain sustainable, ML-driven energy management on a larger scale. * Explainability and interpretability: Address the difficulty of AIEMS, using model interpretability. Develop strategies to provide clear and understandable insights into the decision-making processes of AIEMS models, enabling users and stakeholders to understand and trust the system's recommendations and course of action. This can help overcome some of the black-box characteristics of ML models and facilitate their implementation in practical energy management applications. * IoT and edge computing integration: Explore the integration of AIEMS with edge computing and IoT devices. This reduces latency and enhances responsiveness by facilitating the real-time monitoring, control, and optimization of energy systems at the device or sensor level. Investigate methods for employing edge computing in edge analytics, distributed ML, and decentralized decision-making within AIEMS. * Robustness and cybersecurity: Discuss the difficulties with the robustness and cybersecurity of AIEMS. Develop methods to ensure the security, robustness, and resilience of AIEMS against cyber threats, data breaches, and hostile attacks. This entails examining privacy-preserving measures, secure communication protocols, and anomaly detection methods to safeguard sensitive energy data and ensure the integrity and availability of AIEMS systems. * Human-centric AIEMS: Examine how human-centric methods might be incorporated into the design and use of AIEMS.