## 3 Results
### Detailed Analysis of Papers  
The research query represented in Figure 1 has been designed to incorporate papers featuring both a thermal comfort model and a control system, the latter being expressed with a wide range of methodologies and applications. Figure 5 shows the key blocks of this process: input information is fed to a controller that uses a methodology and the predictions from a thermal comfort model to provide the settings required by a physical indoor environment control system. The same logic was used in keyword analysis, taking the \"Author Keywords\" field in the Scopus-generated database as reference for 144 out of 166 records (empty field in 22 cases). Initially, all unique keywords were extracted and manually classified in the five categories described in Table 2. Categories \"C\", \"S\", and \"M\" somehow overlap with the rationale behind the query: category \"C\" is thermal-comfort-related, while categories \"S\" and \"M\" are expected to be related mainly to control aspects. On the other hand, category \"E\" is not directly included in the search query, but it turns out to be an integral part of thermal-comfort-based control literature. After the classification process, the keyword categories of each paper have been determined to return the chart in Figure 6. For example, bar \"C+E\" indicates the number of papers with at least one keyword \"C\" and at least one keyword \"E\", each paper being only counted once. Keywords in category 5 have been ignored. Figure 4: Breakdown of investigated articles by journal. Figure 5: Blocks of thermal-comfort-based control system. Almost 90% of the papers with reported keywords feature at least a \"C\" keyword. A similar percentage applies to the \"S\" category, and method-related keywords are reported for over 70% of the analyzed works. Although energy is not in the search query, 60% of the papers contain \"E\" keywords, and about 50% have both thermal-comfort- and energy-related keywords. A further confirmation of this bond can be observed in Figure 7, realized in VOSviewer [26]. The size of node items (keywords) is proportional to the number of occurrences, and the link between two items represents the co-occurrence of the two keywords in a document. The distance between groups of keywords (clusters) is the expression of their relatedness. Clusters are automatically identified by the software algorithm, although map creation parameters can be adjusted. In this case, ten clusters were originally returned by VOSviewer, which have been manually reduced to four by changing sub-cluster colors to provide more meaningful information. Three clusters are close to each other and can be associated mainly with HVAC systems and control methods (top-left, in yellow, two sub-clusters merged), to smart buildings and IoT (bottom-left, in olive, four sub-clusters merged), and to thermal comfort and energy (middle, in indigo, three sub-clusters merged). The fourth cluster (right, in cyan, single cluster) describes works focusing on ventilation, which appear to form a cluster on its own. Keyword clusters do not overlap perfectly with the categories presented in Table 2, but some results are consistent. For example, it is confirmed that thermal comfort studies often include energy considerations, and that control issues can be discussed from the viewpoint of systems and/or methods (HVAC and IoT clusters).