## 4 Results
### Dataset  
Skelleftea Kraft of Sweden provided us with the hourly energy consumption statistics for 62 residential apartments in the city of Skelleftea in Sweden [88]. The interior space of this dataset is 58 square meters (sqm) on average, with a minimum of 23 square meters (sqm) and a high of 142 square meters (sqm). Some of these apartments run on the electric heating method and the others on the district heating method. Each of these units is 2.40 m high. Energy data from 1 January 2022 to 31 December 2022, expressed in kWh, are included in this anonymized dataset. This dataset served as our initial training dataset with labels.  
This 62-apartment dataset contains hourly energy consumption value of 24 h for 365 days, which means it has total (62 \(\times\) 24 \(\times\) 365) = 543,120 rows. We then preprocessed this dataset by excluding the apartments with same interior space. This resulted in only 13 apartments with unique values of interior space. We replaced the missing hourly energy consumption values of these 13 apartments with mean value of previous and next hour's energy consumption. We then combined these 13 apartments' interior space values with three different solar illumination values (Table 2) and seven different occupancy values (Tables 3 and 4), resulting in a dataset of (13 \(\times\) 3 \(\times\) 7) = 273 rows. To train and test our proposed framework, we divided these 13 apartments into two parts: 10 training apartments, and 3 testing apartments. Our weakly augmented pseudo-labeled dataset contains 191 apartments with unique interior space, resulting in (191 \(\times\) 3 \(\times\) 7) = 4011 rows. The strongly augmented pseudo-labeled dataset contains (951 \(-\) 191) = 760 apartments with unique interior space, resulting in (760 \(\times\) 3 \(\times\) 7) = 15,960 rows. Thus, our extended training dataset, consisting of labeled as well as weakly and strongly augmented pseudo-labeled data, has (10 + 191 + 760) = 961 training apartments. This means that only 1% of this extended dataset is labeled, and the remaining 99% is pseudo-labeled. We show the number of rows of the labeled, pseudo-labeled, and extended datasets in Table 9. We show the histogram of number of instances across different interior space ranges in various types of dataset in Figure 5. According to Figure 5a, the data distribution in initial labeled dataset (preprocessed) is uneven, with the majority of interior space instances concentrated in the (23, 33) and (73, 83) ranges. Several intervals, such as (33, 43), (43, 53), (63, 73), (83, 93), and (103, 200) contain no instances of interior space. Such imbalance indicates potential bias in the dataset. The weakly augmented pseudo-labeled dataset (Figure 5b) shows a near-uniform distribution across all bins, suggesting an even representation of interior space values introduced through the weak augmentation. The same near-uniform distribution is visible in strongly augmented pseudo-labeled dataset (Figure 5c), with more frequent instances across interior space ranges. The extended dataset, consisting of both labeled and pseudo-labeled data (Figure 5d), shows the highest number of instances across different interior space ranges.  
Figure 5: Histogram showing the number of instances against interior space intervals in (**a**) initial labeled dataset (preprocessed), (**b**) weakly augmented pseudo-labeled dataset, (**c**) strongly augmented pseudo-labeled dataset, and (**d**) labeled and pseudo-labeled dataset.