# Machine Learning in Smart Buildings: A Review of Methods, Challenges, and Future Trends
## 8 Limitation, Challenges, and Opportunities of Existing Solutions
### Open Issues and Challenges for Intelligent Energy Management Systems in Buildings  
An effective IEMS must be scalable, secure, cost-effective, and dependable. Figure 5 describes unresolved concerns and challenges pertaining to IEMS in buildings, which are elaborated below. * Scalability: In smart buildings, \"scalability\" denotes the ability to expand. The extension of this building includes the incorporation of new modules and other devices [34]. It is essential to ensure the power quality of the EMS anytime new services, applications, or devices are integrated [126]. An unscalable EMS is incapable of accommodating development, ultimately leading to unreliability and necessitating replacement. The EMS in smart buildings must be scalable due to the increasing number of consumers and demands [127]. * Privacy and security: Privacy and security are interrelated concepts. Inadequate security renders the EMS vulnerable to illicit exploitation and jeopardizes consumer privacy. Ensuring a high level of privacy and security in smart buildings is particularly problematic due to the absence of common standards for IoT security [126]. The collection and analysis of data is a critical component of an IoT-based EMS. The bulk of this information is accumulated over time through energy consumption and maintenance activities. Decisions on the efficient functioning of buildings are made upon the analysis of data. A comprehensive data management system can evaluate a building's energy efficiency; however, information integrity, mutual trust, and authentication are the three crucial security criteria. * Performance management: IoT-based smart buildings contain billions of internet-connected devices. These devices must be governed by a system that guarantees adequate fault tolerance and detection. Consequently, it is essential to implement a service that manages IoT device configuration, communication, and accessibility across different user tiers [128]. * Cost-effectiveness: The energy consumption in smart buildings is associated with various expenses. These expenditures encompass the cost of the instruments, their operation, the expense of technological services, and the maintenance fees. Moreover, there is an expense linked to the incorporation of diverse energy sources into an EMS. On the one hand, consumers are directly impacted by the high price of an EMS. On the other side, EMS performance may suffer if costs are cut by employing less expensive materials. For this reason, it is imperative to keep both in balance. Currently, most private customers cannot afford many IoT solutions because they are too expensive for smart buildings and other industrial applications [129]. \begin{table}
\begin{tabular}{c c c c} \hline \hline \multicolumn{1}{c}{**Challenge/Opportunity**} & \multicolumn{1}{c}{**Description**} & \multicolumn{1}{c}{**Impact on Energy Prediction**} & \multicolumn{1}{c}{**Proposed**} \\  & & **and Management** & **Solutions/Directions for** \\ \hline \multirow{4}{*}{Cybersecurity and Privacy} & Protecting the integrity and confidentiality of data used in ML applications. & Essential for maintaining trust in energy management systems and protective sensitive information. & Develop secure ML frameworks and privacy-preserving algorithms; emphasize cybersecurity in system design. \\ \cline{2-4}  & Adhering to evolving standards and regulations & Compliance with regulations can limit the scope of ML applications in energy management. & Engage in policy development; design flexible ML systems that can adapt to regulatory changes. \\ \hline \hline \end{tabular}
\end{table} 
Table 10: _Cont._* Big data processing: In IoT-based systems, a growing volume of data needs efficient handling and processing. Using conventional techniques and equipment for data processing is not practicable. Real-time data is gathered and analyzed in IoT-based smart buildings to assist in decision-making. As a result, processing calls for new techniques and equipment [130]. Some of these technologies employ local data processing.