# Machine Learning in Smart Buildings: A Review of Methods, Challenges, and Future Trends
## 3 Work Methodology  
This systematic review was conducted in strict adherence to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) reporting standard, ensuring transparency and rigor in the methodology. Records for inclusion were identified through comprehensive searches conducted on the Scopus database, spanning the period from 2012 to April 2025. The search query employed was specifically designed to capture relevant literature: TITLE-ABS ((\"Machine Learning\") AND (\"Smart Building\") AND (\"Energy Management\" OR \"Efficiency\" OR \"Occupancy\") AND PUBYEAR > 2011 AND PUBYEAR < 2026). Following the initial search, the titles and abstracts of the identified records were screened against four pre-defined inclusion criteria: (i) the source had to be peer-reviewed, (ii) the study needed to focus on smart-building applications, (iii) there had to be explicit use of an ML algorithm, and (iv) the study had to include an empirical evaluation of outcomes related to energy, comfort, or operations. The full texts of the remaining eligible articles were then assessed for final inclusion. Studies were excluded if they lacked quantitative results. During the data extraction phase, comprehensive information was captured, including publication metadata, the specific ML technique employed, characteristics of the dataset used, and key performance indicators such as energy-saving rates or prediction accuracy. Figure 4 shows the PRISMA diagram summarizing the identification, screening, eligibility assessment, and final inclusion of machine learning studies on smart-building energy management published between 2012 and April 2025. The studies included in the review cover many application domains within smart buildings, such as energy prediction, system control, and occupant behavior analysis. Some of these works are shown in Table 2. \begin{table}
\begin{tabular}{c c c c c} \hline
**Metric Name** & **Description** & **Importance** & **Formula** & **Typical Values** \\ \hline \multirow{4}{*}{RMSE (Root Mean Square Error)} & Measures the average magnitude of the errors between predicted and actual values. & Indicates the model’s accuracy in energy prediction; lower values signify better performance. & \multirow{2}{*}{\(\sqrt{\frac{1}{n}\sum_{i=1}^{n}(y_{i}-\hat{y}_{i})^{2}}\)} & 0–10\% of range of target variable \\ \cline{1-1} \cline{3-5}  & Average of the absolute errors between predicted and actual values. & Shows the model’s precision in energy prediction without considering error direction. & \multirow{2}{*}{\(\frac{1}{n}\sum_{i=1}^{n}|y_{i}-\hat{y}_{i}|\)} & 0–10\% of range of target variable \\ \hline \multirow{4}{*}{Accuracy} & Proportion of correctly predicted instances to total instances. & Important for classification tasks in energy management, like fault detection. & \multirow{2}{*}{\(\frac{Number\ of\ correct\ predictions}{Total\ number\ of\ predictions}\)} & \multirow{2}{*}{90–100\%} \\ \cline{1-1} \cline{3-5}  & & Balances the trade-off between precision and recall in classification models. & & \\ \hline \end{tabular}
\end{table} 
Table 1: Performance metrics.