# Machine Learning in Smart Buildings: A Review of Methods, Challenges, and Future Trends
## 4 ML Techniques in Energy Prediction
### Supervised ML Solutions
\begin{table}
\begin{tabular}{p{14.2 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt} p{142.3 pt}} \hline
**Category** & **Type** & **Algorithmes** & **Pres** & **Cons** & **Applicability in IEMs** \\ \hline \multirow{9}{*}{Construction} & \multirow{3}{*}{Classification} & \multirow{3}{*}{Normal networks} & \multirow{3}{*}{Resquires little statistical training:Con detect complex non-linear relationships} & \multirow{3}{*}{Resquires little statistical training:Con detect complex non-linear relationships} & \multirow{3}{*}{Resquires little statistical training:Con detect complex non-linear and 34 old data} & \multirow{3}{*}{Used for classification, control and automated outlier rate a long time and 34 old data} & \\ \cline{3-5}  & & & & & & \\ \cline{3-5}  & & & & & & \\ \cline{3-5}  & & & & & & \\ \cline{3-5}  & & & & & & \\ \cline{3-5}  & & & & & & \\ \cline{3-5}  & & & & & & \\ \hline \multirow{9}{*}{Supervised Learning} & \multirow{3}{*}{Classification} & \multirow{3}{*}{Normal networks} & \multirow{3}{*}{Resquires little statistical training:Con detect complex non-linear relationships} & \multirow{3}{*}{Used for classification, control and automated outlier rate a long time and 34 old data} & & \multirow{3}{*}{Used for classification, control and automated outlier rate a long time and 34 old data} & \\ \cline{3-5}  & & & & & & & \\ \cline{3-5}  & & & & & & \\ \cline{3-5}  & & & & & & & \\ \cline{3-5}  & & & & & & & \\ \cline{3-5}  & & & & & & & \\ \cline{3-5}  & & & & & & & \\ \hline \multirow{9}{*}{Supervised Learning} & \multirow{3}{*}{Classification} & \multirow{3}{*}{Ingestion networks} & \multirow{3}{*}{Virty simple representation does not allow for rich hypotheses hypotheses} & \multirow{3}{*}{Should be trained on a large training set.} & \multirow{3}{*}{Iserved mappingement system and human activity recognition.} & \multirow{3}{*}{Astivity recognition.} & \multirow{3}{*}{Astivity recognition.} & \multirow{3}{*}{Astivity recognition.} & \multirow{3}{*}{Astivity recognition.} \\ \cline{3-5}  & & & & & & & & \\ \cline{3-5}  & & & & & & & \\ \cline{3-5}  & & & & & & & \\ \cline{3-5}  & & & & & & & \\ \hline \multirow{9}{*}{Supervised Learning} & \multirow{3}{*}{Expression} & \multirow{3}{*}{Deep Learning} & \multirow{3}{*}{ResNet training of feature other than hard knowledge} & \multirow{3}{*}{Resquires training using annotated data: Many knowledge variations in novel structureRecently, supervised learning methodologies have emerged as a dominant approach to tackling tasks related to smart building energy management systems, mainly due to their high accuracy when trained on extensive, well-annotated datasets. Methods like classification and regression models have demonstrated efficacy in forecasting energy use, detecting occupancy, and diagnosing faults. The challenges of supervised learning underscore the potential benefits of incorporating unsupervised and reinforcement learning approaches. Unsupervised learning may discern trends and anomalies without labeled data, rendering it appropriate for detecting anomalies in energy consumption patterns or tenant behavior. On the other hand, reinforcement learning is an interactive, self-improving process that can adapt to changes in how the building is used or to variables in the outside environment. This makes solutions more reliable in complex, changing situations. Diversifying ML methodologies in smart energy systems enhances model flexibility and robustness, transitioning from static forecasts to adaptive, real-time decision-making frameworks.