## 4 AI-Based Approaches in Smart Buildings
#### 4.1.9 Support Vector Machines (SVMs)  
Supervised learning algorithms include support vector machines (SVMs) that use vector support machines. By estimating the occupancy rate, their goal is to be able to predict the current state of the facility in terms of facility management, resident satisfaction, and overall security and safety [84]. Aiming to find the hyper plane with the highest margin of separation between two categories, the SVM algorithms attempt to divide the data into two groups: both occupied and empty [85]. To classify non-differentiable pair data, non-linear kernel functions, such as radial basic functions, can be utilized to describe the data in larger dimensions and subsequently be used for classification. Classifying data is the most typical use case for a support vector machine. Finding a function for all the training data, especially information regarding the target, is the primary goal of these techniques. Temperature, humidity levels, and the strength of the sun's radiation should all be taken into account.  
In the following Table 2, different kinds of machine learning algorithms and the objectives of these algorithms are discussed with their use in IoT applications to make smart buildings more energy efficient.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt} p{56.9 pt}} \hline \hline
**Sr.** & **Machine Learning Models/Algorithms** & **Objectives in IoT Technologies** & **Smart Buildings Applications Domain** & **Advantages** & **Disadvantages** \\ \hline
**1** & ANNs & Forecasting and Modeling. & Reduce energy based on intelligent sensors. & Excellent accuracy and comfortable monitoring & Complex \\ \hline
**2** & WNN & Forecasting events based on time series data. & Used in building lighting and shading systems. & Excellent consistency & Low speed \\ \hline
**3** & Deep learning & It is helpful in both the prediction of data and the modeling of patterns. & Useful for designing and modeling energy-efficient systems. & High precision and acceptable speed & Very complicated \\ \hline
**4** & Time Series Analysis & High dimensionality. & Generates reliable prediction results for building energy. & Predict the Future & The observations are not independent of one another \\ \hline
**5** & Regression & Behavior prediction. & Discover the environmental and physical variables that contribute to smart building energy & Rapid speed & Unreliable precision \\ \hline
**6** & Deep Reinforcement Learning & Systematic decision-making. & It has the potential to address the difficulties of energy efficiency in smart buildings. & Solve complicated tasks & Very complicated \\ \hline
**7** & Decision Tree Classification & Maps several decisions. & Predicts the danger of an outage and handles energy management and energy usage in smartbuildings. & Simple to understand & Relative inaccurate \\ \hline
**8** & Genetic Algorithms & Optimization of issues. & Managing loads optimally and enhancing energy efficiency. & Excellent accuracy & Low speed \\ \hline
**9** & Support vector Machines & The organization of data and the protection of it in IoT. & Prediction of the amount of energy used in buildings. & Excellent accuracy & It is complex and the speed is low \\ \hline \hline \end{tabular}
\end{table}
Table 2: Machine learning algorithms, the objective of IoT technologies, and their domain in smart building applications.