# 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
Eventually, it results in discrepancies between the demands of practice and the conclusions of recent studies. Researchers should adopt a problem-driven research paradigm that considers all steps in the application process of ML models to effectively promote the use of ML technology to build energy efficiency measures. * Lack of model adaptability: Model adaptability denotes the capacity of ML models to be efficiently integrated into current personnel and building energy management systems. Most recent studies were performed in controlled environments and did not consider the practical contexts of application. Building propriators encounter difficulties in effectively utilizing ML-based energy forecasting models due to insufficient adjustability. The applicability of the experimental environment's data structure and data-cleaning processes remains questionable. Researching effective ways for the integration of ML models into existing building energy management systems is essential. The majority of building management personnel presently lack the requisite expertise in building science and data science essential for engaging in the deploy \begin{table}
\begin{tabular}{c c} \hline \hline
**Data Type** & **Examples** \\ \hline Meteorological information & Weather-related data include temperature, humidity, wind speed, solar radiation, and precipitation. \\ \hline Indoor environmental information & Indoor environment of a building, such as indoor temperature, air quality parameters (CO\({}_{2}\) levels, volatile organic compounds), and lighting conditions. \\ \hline Occupancy-related data & Occupancy of a building, including the number of occupants, their activities, and their behavior patterns. \\ \hline Time index & Timestamp or time index associated with the collected data, enabling temporal analysis and correlation with other variables. \\ \hline Building characteristic data & Relative compactness, surface area, wall area, roof area, overall height, orientation, glazing area, building envelope heat transfer coefficient, window-wall ratio, and shading coefficient. \\ \hline Socioeconomic information & Income, electricity price, GDP, and population. \\ \hline Historical data & Past energy consumption records, including historical energy usage patterns, utility bills, and historical weather data. \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Major data types used in ML-based building energy modeling. ment of ML models. Current research has failed to yield \"plug-and-play\" ML models or user-friendly solutions that can be readily adopted by existing personnel. Thus, transforming current structures into ML-based energy management systems continues to pose difficulties. To resolve this issue, it may be imperative to recruit additional technically proficient workers and augment the knowledge of existing building management experts. This knowledge gap has considerably impeded the implementation of ML-based energy forecasting models. Consequently, establishing a consistent and user-friendly installation methodology for ML models is essential for enhancing their adaptability in building management. * Insufficient focus on data privacy and security: A significant yet undetermined challenge in implementing ML to enhance building energy efficiency is data privacy and security, especially considering the widespread utilization of IoT devices in smart buildings. These systems persistently collect and evaluate sensitive data, encompassing details on tenant behavior, energy consumption patterns, and operational metrics from diverse, interconnected devices. In the absence of comprehensive security standards, this data is susceptible to unwanted access and cyberattacks, compromising individual privacy and the operational integrity of the facility. Data privacy issues are amplified when smart buildings frequently consolidate information from various sources, such as HVAC systems, lighting controls, and occupancy sensors.