# Intelligent Thermal Comfort Controlling System for Buildings Based on IoT and AI
## 2 Literature Review
### Artificial Intelligence Algorithms For Building Energy Prediction
#### 2.1.4 Machine Learning (ML) Algorithms  
Predicting occupants' thermal comfort by ML algorithms is another key methodology related to artificial intelligence algorithms. [PERSON] and [PERSON] introduced a non-invasive approach for automatic prediction of personal thermal comfort for real-time feedback with ML [14]. [PERSON] et al. proposed an indoor-climate control framework to decrease the disparity between energy-efficiency and indoor thermal-comfort in buildings, which comprises two main components: a thermal-comfort prediction model, and an optimization algorithm termed as the optimal air temperature (OAT) algorithm [15]. [PERSON] et al. built a data-driven simulation model for thermal comfort based on k-nearest neighbor (KNN), random forest (RF) and SVM, which they simulate set-point control system with [16]. [PERSON] and [PERSON] introduced a deep learning-based framework for energy optimization and thermal comfort control in smart buildings by considering the energy consumption of HVAC and the thermal comfort of the occupants [17]. [PERSON] et al. investigated an intelligent ensemble machine learning (EML) method Bagging for thermal perception prediction. They compared Bagging approach with ANN and SVM regarding conventional statistical indicators [18]. [PERSON] et al. proposed a personal comfort model to predict individuals' thermal preference by using a new type of feedback, occupants' heating and cooling behaviour with a personal comfort system (PCS) to improve occupant satisfaction and energy use in buildings [19]. [PERSON] et al. suggested a methodology by using a camera network to assess thermal comfort in multi-occupancy spaces with defining characteristics of the non-intrusive thermal comfort detection [20]. [PERSON] and [PERSON] presented a new prediction method for thermal comfort indices by using an improved SVM with more adequate nonlinear kernels to obtain predicted values with a polynomial kernel [21]. [PERSON] and [PERSON] developed a platform for simulating occupants' thermal sensations and applied it to examine the performance of SVM on thermal comfort prediction. They also proposed a hybrid SVM-LDA thermal comfort classifier to improve the efficiency of model training [22]. [PERSON] et al. applied ML to control indoor thermal comfort directly with high accuracy to achieve smart building control and operation [23]. [PERSON] et al. presented a decision support system to predict human thermal comfort in real-time then suggest corresponding actions. They also introduced a novel ML-based approach to for individual's thermal comfort model [24]. [PERSON] et al. used multiple methods to assess occupants' thermal comfort through the collection of feedback. Their experiment was the indoor environment in steady-state conditions (PMV and PPD) with the use of nearly and wearable solutions. The method was applied by classification and regression tree (CART) algorithm for machine learning with testing in real offices involving eight workers [25]. [PERSON] et al. summarized the recent literature during the period of 2016-2019, from perspectives of predicted comfort indices, algorithms applied, input features, data sources, sample size, training proportion, predicting accuracy, etc. [26].