## 4 Results
### Results  
With our proposed semi-supervised explainable BRBES framework, we performed regression instead of classification. It is not always feasible to do semi-supervised regression with the clustering assumptions used in semi-supervised classification [89]. As a result, the majority of semi-supervised classification techniques are not suitable for regression directly [89]. Due to the simplicity of data augmentation, a lot of semi-supervised learning techniques are used in language and image datasets [53]. However, our numerical energy consumption data are in tabular format, which lack context. Such a lack of context impedes augmentation due to significant information loss after augmentation [53]. Moreover, traditional data augmentation methods generate too much noise on tabular data. Therefore, due to regression on numerical tabular data, our proposed framework is not directly comparable with traditional semi-supervised learning methods, such as the consistency regularization methods, FixMatch, MixMatch, and ReMixMatch [17]. We compared our proposed semi-supervised explainable BRBES framework with four state-of-the-art semi-supervised models: Support Vector Regressor (SVR), Linear Regressor (LR), Multi-layer Perceptron (MLP) regressor, and Deep Neural Network (DNN). For the SVR, the kernel is the Radial Basis Function (RBF), the regularization parameter (c) is 100, the kernel coefficient (gamma) is 0.10, and epsilon is 0.20. For the LR, both the y intercept (\(b_{0}\)) and slope (\(b_{1}\)) are based on the least squares method. Our MLP regressor consists of two hidden layers, with six neurons per hidden layer and a dropout of 0.40. For the MLP regressor's activation function and optimization function, we used Rectified Linear Unit (ReLU) and Stochastic Gradient Descent (SGD), respectively. For this regressor, we ran 50 epochs, with a batch size of 70, resulting in (20,181/70) = 288 iterations per epoch over the extended training dataset. For the DNN, we used eight hidden layers, each having 24 neurons. We used the same activation and optimization function as the MLP in the DNN, with a dropout of 0.50. We ran 100 epochs in the DNN, with a batch size of 70, leading to (20,181/70) = 288 iterations per epoch. Initially, we trained these four models with the labeled training dataset consisting of 10 apartments. To lessen selection bias and prediction variance, we performed five-fold crossvalidation to the original labeled dataset of 13 apartments. We then assigned pseudo-labels to the weakly and strongly augmented unlabeled dataset of 951 apartments using each of these four supervised models. These four supervised models were then retrained with extended training dataset, consisting of both labeled and concerned pseudo-labeled data. Thus, these four models transitioned from supervised to semi-supervised. According to Table 10, the InfoNCE loss value of the initial BRBES was only 0.16, which is very low. To improve the accuracy of the initial BRBES, we employed JOPS to optimize its parameters and structure using initial labeled data. In Table 11, we analyze the sensitivity of the accuracy of BRBES to its parameters and structure [90]. We show the effect of optimizing each of the three parameters and the structure of the rule base on the accuracy of the BRBES in this table, where P1 = rule weight, P2 = each antecedent attribute's weight, P3 = consequent attibute's belief degrees, S1 = two referential values \begin{table}
\begin{tabular}{l l l} \hline
**Dataset** & **Training** & **Testing** \\ \hline Labeled (preprocessed) & (\(10\times 3\times 7\)) = 210 rows & (\(3\times 3\times 7\)) = 63 rows \\ Weakly augmented pseudo-labeled data & (\(191\times 3\times 7\)) = 4011 rows & — \\ Strongly augmented pseudo-labeled data & (\(760\times 3\times 7\)) = 15,960 rows & — \\ Extended dataset & (\(210\ +\ 4011\ +\ 15,960\)) = 20,181 rows & — \\ \hline \end{tabular}
\end{table} 
Table 9: Labeled, pseudo-labeled, and extended datasets.