## 3 Results
### Control Strategies  
As outlined in Figure 5, thermal-comfort-based control is performed by means of methods and algorithms which use inputs and thermal comfort information to define settings and actions that can be passed to the physical system. To examine the algorithms adopted in the selected papers, the following macro-categories of methods have been identified that can be used individually or combined with each other:  
* Rule-based (RB): settings are determined with knowledge-based rules. * Model-predictive control (MPC): a model predicts the system state on a desired time horizon and finds optimal actions minimizing an objective function. * Machine learning (ML): models are based on continuous data collection. * Optimization (O): optimal settings are obtained by minimizing an objective function. * Mathematical model (MM): settings are the solution of a mathematical equation or system of equations. An overview of the techniques is given in Figure 9, where a slight prevalence of rule-based and optimization methods is observed. RB algorithms include strategies in which the proper action is chosen based on a set of knowledge-based simple instructions. However, the category also includes fuzzy rule-based systems, such as in [78; 113; 117; 118; 119]. Optimization allows to calculate output quantities by minimizing a cost function subject to constraints. In these methods, thermal comfort can be in the cost function or in the set of constraints, typically combined with energy-saving objectives. Examples of techniques used in the analyzed papers are particle swarm optimization, genetic algorithms, cuckoo search, proximal policy optimization, gray-wolf optimization, and firefly algorithms. Together with MPC and ML methods, optimization usually requires elaborate mathematical formulation and attention to computational resources. Finally, MM indicates any type of physics-based description that is developed for control purposes; examples range from simple functions, such as adaptive models to directly calculate comfort temperature, to large sets of equations describing building or HVAC systems used in optimization or rule-based processes. In 40% of the occurrences, two techniques have been used together, the most frequent occurrence being the combination of a mathematical or machine learning model with optimization techniques (Figure 10). ML is generally used to build a model instead of a physics-based solution. Therefore, no combination can be found of MM and ML in Figure 10. It is worth noting that the MPC definition includes both a model and an optimization stage. This is the reason why no combination of MPC + MM, nor of MPC + O, is present in Figure 10, either. MPC exploiting a physics-based model is adopted in almost one-fifth of the studies. The use of an ML-based model instead of a physics-based model in an MPC system is indicated by ML + MPC; if compared with its physics-based counterpart, this solution is exploited in one third of the cases. Figure 9: Methods used to determine control settings in the analyzed papers. (**Left**): occurrences of each method; (**right**): method combinations. Such methods have been used to calculate the values of a wide range of control variables, as summarized in Figure 11. Temperature set-points are the most popular choices, mainly as room--air, operative, thermostat, or, generically, indoor--temperature, but also in terms of HVAC working parameters (for example, supply air [120,121] or water [122,123] temperature). In air conditioning systems, some authors proposed to operate on the air flow by determining air flow rate setting or fan speed, the former being mainly used in central systems (e.g., [34,124]) and the latter in personal devices (e.g., [125,126]). Several studies exploited window control for natural ventilation (e.g., [33,87,95,118]) or solar shading (e.g., [127,128,129,130]).