# Wireless Sensor Networks for Building Information Modeling
## 5 Sensor Data from the Pilots
In this case, the higher temperature values (over 25 \({}^{\circ}\)C) are well-captured during the high-power consumption days (indicated by the AC power spikes), while these are maintained at about 24-25 \({}^{\circ}\)C during the days that the power consumption remains relatively low. Concerning the Spanish validation site, Figure 10 presents temperature data obtained by different sensors within the same apartment. Similar to the Greek pre-validation site, the obtained data indicate the seamless sensor-data flow for the period 5 November 2021-15 November 2021, during which the room temperature adapted in accordance with the occupants' heating needs. Figure 11 demonstrates, respectively, the total power consumption measured for the whole apartment. An example of the sensor-temperature-data mapping for the second validation site (in Poland) is also shown in Figure 12 for the same period. Figure 9: Greek pilot site’s temperature sensor (left axis—purple color) and AC units power (right axis—green color) consumption data, 15 September 2021–25 September 2021. Figure 11: Spanish-pilot-site apartment’s total power consumption data, 5 November 2021–15 November 2021. Figure 10: Spanish pilot site’s temperature data from different sensors from the same apartment, 5 November 2021–15 November 2021. The aforementioned sensor data streams are subsequently used by the BIMERR toolkit to train the OB models, as discussed in the following section. A Use-Case Example of WSN Measured Data for BIM: Training Occupant Behavior Models to Increase the Energy-Performance Estimation Accuracy  
The methods applied to train occupant behavior models by PRUBS fall under three main categories: (1) the occupant thermal comfort, (2) the HVAC systems' thermostat adjustment and (3) the profiling of other electric components' usage. A brief description is provided in the following sections; meanwhile, for a detailed description, the reader should refer to Reference [28]. For the occupant thermal comfort modeling, the Gaussian Naive Bayes method [33] is applied, a supervised machine-learning algorithm that follows the [PERSON] theorem, assuming conditional independence between the features, named Drivers in terms of obXML. Before training the model, an event-generation process is applied to populate the proper training events/dataset required as input to the ML model. In that process, certain preprocessing rules/steps are applied to sensed data that are stored in the PRUBS database. It is worth mentioning that other methods have also been evaluated as candidate alternatives to the Gaussian Naive Bayes classifier for the thermal-comfort profiling, such as the Multinomial Naive Bayes, Logistic Regression, Random Forest, MLP Classifier (Neural Networks), SGD Classifier (Stochastic Gradient Descent), SVM (Support Vector Machines) and K-Neighbors Classification, [33]. A comparison was performed; however, such an analysis is beyond the scope of this paper. For the HVAC systems' thermostat-adjustment behaviors, two statistical modeling approaches that have been introduced in recent studies and seem to be the most applicable to residential buildings [23, 24] are implemented in PRUBS. In the first approach [23], occupants are clustered into active, medium and passive users and multivariate logistic regression models are trained to predict the likelihood of a set-point increase or decrease. In the second approach [24], a three-parameter discrete Weibull distribution model is being developed and trained to represent occupants air-conditioning usage, considering indoor temperature, CO\({}_{2}\) concentration and occupancy state as the model Drivers. Figure 12: Polish pilot site’s temperature data from different sensors in the same apartment, 5 November 2021–15 November 2021. The Gaussian Processes [25] modeling approach was selected to extract information for the Other Electric Equipment Usage.