# Machine Learning in Smart Buildings: A Review of Methods, Challenges, and Future Trends
## 8 Limitation, Challenges, and Opportunities of Existing Solutions
### Practical Challenges in Applying ML for Building Energy Efficiency
With the transmission and processing of this information, there is an increasing necessity for secure data management methods and encryption protocols to safeguard it against potential dangers. Insufficient data security measures jeopardize sensitive information and undermine occupant faith in smart building systems. Consequently, the implementation of robust data security protocols is essential. This must encompass end-to-end encryption, robust authentication methods, and periodic audits to guarantee data protection at all phases--from collection and transmission to storage and analysis. Resolving these privacy and security concerns is essential for cultivating a secure and reliable atmosphere for ML-based energy management in smart buildings, promoting broader acceptance while protecting occupants' rights and enhancing resilience against cyber threats [108]. * Lack of user confidence: The application of ML in building energy management directly influences the return on investment (RoI). Users employ ML models for building energy management passively due to this critical factor. ML marketing necessitates a substantial level of trust from users because of its status as an emerging technology [1]. Prospective users sometimes exhibit \"wait-and-see\" dispositions when adopting new technology. The domain of building energy management is one where this is highly prominent. Users adopt contemporary technology solely when they perceive it as providing tangible benefits. Clients exhibit a deficiency of confidence in the ongoing efforts of ML-based building energy efficiency. The current research predominantly evaluates the fitting capabilities of ML algorithms using building energy data without specific instances of how these methods might improve energy efficiency. Consequently, users struggle to evaluate the cost-effectiveness of ML-based energy-saving solutions, impeding their readiness to embrace this new technology. This difficulty is especially pertinent for older structures, necessitating considerable enhancements to their monitoring systems to adhere to the digital norms of ML-based energy efficiency protocols. Exhibiting the energy conservation advantages and financial feasibility of various implementations may motivate prospective users to choose ML-based solutions. * Urban-Scale Variability and Model Transferability: A significant constraint in machine learning-based energy modeling is its susceptibility to urban-scale variables,including building density, surface area, and morphological layout. These contextual factors greatly influence energy consumption behavior, presenting difficulties for the reliability and applicability of prediction models in diverse urban settings. Recent research by [124] revealed that building energy simulations can produce significantly divergent outcomes when conducted in isolation compared to those integrated within a dense urban environment. In their case study conducted in Montreal, the inclusion of adjacent building shade augmented winter heating demands by as much as 44% and diminished summer cooling requirements by up to 40%. These findings emphasize the significant influence of urban-scale shading on model results and stress the necessity of considering context in predictive modeling [124]. In addition, ref. [125] presented a tiled, multi-city dataset that facilitates the cross-comparison of urban energy simulations. Their dataset serves as a crucial resource for assessing the transferability of machine learning models trained in one urban typology to different ones. It examines the variability caused by population density and surface area in metropolitan situations, facilitating the creation of more scalable, adaptive models [125]. Collectively, these studies highlight the need for site-specific calibration and the incorporation of adaptive learning methodologies, such as domain transfer or federated learning, to guarantee scalability and precision across varied urban environments. Table 10 summarizes the challenges that affect energy efficiency.