# Hierarchical Modelling for CO\({}_{2}\) Variation Prediction for HVAC System Operation
## 5 Experimental Setup
### Experimental Parameters
This representation accentuates the small differences in such windows, reflected by the colour intensities of the GAF representation. On the other hand, the \"holistic\" approach calculates the minimum and the maximum over the whole datasets of a single environmental feature. The resulting GAF images are in concert with the dynamics in the whole dataset, which means that the images' colour intensities mirror the differences in values in a \(w\) compared to value differences over the whole dataset. Both of these approaches are investigated in the experimental procedure. The existence of outliers is fundamental in the GAF representation, affecting the accuracy of CO\({}_{2}\) variation predictions. The EDA provided interesting insights into each environmental feature. Features such as CO\({}_{2}\), CO\({}_{2}\)_PIR, and humidity displayed highly skewed distributions with large differences between the minimum and maximum values, to the detriment of the \"holistic\" approach. If these features are normalized based on these extremes, the majority of the data will fall within a small margin, and the resulting differences in GAF images will be minuscule. As a result, the CNNs will fail to infer any meaningful relationship from the GAF representation. A log transformation is applied to these features to mitigate the effect of outliers and to shrink the values' ranges. Other features, such as pressure and temperature features displayed little variations. The exponential function is applied to each of these features to amplify these small differences. These concerns are not shared with the \"local\" approach, since its GAF representation depends on the minimum and maximum \begin{table}
\begin{tabular}{c c c} \hline \hline
**History (Minutes)** & **Future (Minutes)** &
\begin{tabular}{c} **Training Data** \\ **Testing Data** \\ \end{tabular} \\ \hline h-5 & f-5 & 334,677 \\  & 74,730 \\ \hline h-10 & f-10 & 332,667 \\  & 74,514 \\ \hline h-15 & f-15 & 330,657 \\  & 74,300 \\ \hline h-20 & f-20 & 328,647 \\  & 74,085 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Dataset Sizes. calculated in a time window. Therefore, the differences in feature values will be mirrored in the GAF representation despite their value ranges resulting from any transformation. **Individual CNN Models**: The research community provides ample image recognition models for individual learners that can be applied to the generated datasets. These pre-trained CNN models include VGG16 and VGG19 [43], ResNets [44], ResNet_inception [45], Inception [46], and Xception [47] with different respective architectures. The experimental procedure evaluated all these CNN models. Keen readers can find the details of their respective architectures in the papers listed with every CNN model. Each of their architectures includes many convolutional and Fully Connected Layers (FCL). In the experimental procedure, the last layer that outputs the classification result is removed, given that a different dataset with different outputs is used in this study. Therefore, the remainder of the layers is augmented with a global average pooling layer and Fully Connected Layers (FCL). The obtained CNN models include either no FCLs, one FCL, or two FCLs. The number of neurons of these FCLs ranges from 64 to 4096 neurons. **Ensemble Learner Algorithms**: Different algorithms were evaluated for the ensemble learning step. In particular, the same algorithms used in FECOP were evaluated following HMCOVP. These ML algorithms include RF, DT, RR, and DNNs. This step is followed to emphasize the distinction between the two methodologies in terms of the validity of feature engineering techniques. **Transferability Parameters**: The transferability of the developed models is evaluated by applying them to a small room, denoted by room01, that fits two people. When the results were unsatisfactory, part of the training data of the target room is used to tune the parameters of the source room's model. The fraction of the used training data is \(\{0.1,0.25,0.33\}\).