## 4 Results and Discussion
### Accuracy of the MC-LSTM Model  
To assess the performance of the proposed MC-LSTM model, an ablation study was conducted comparing the MC-LSTM with a single LSTM model using multiple evaluation metrics. The test set consisted of power consumption data from 1 June 2023 to 31 May 2024--data that were not included in the training set. Figure 6 compares the predicted power consumptions of LSTM and MC-LSTM (yellow and blue solid lines, respectively) with the actual consumption (red dashed line).  
The prediction accuracy was evaluated using the mean arctangent absolute percentage error (MAAPE), calculated as follows [44; 45]:  
\[\text{MAAPE}=\frac{1}{n}\sum_{i=1}^{n}\arctan\biggl{(}\frac{|y_{i}-\hat{y_{i}}| }{|y_{i}|}\biggr{)}. \tag{8}\]  
To interpret the MAAPE values, the metric was converted into a percentage and then subtracted from 100% to intuitively represent prediction accuracy. The accuracy for the MC-LSTM models in each cluster was 98.21%, 97.51%, 97.73%, and 98.27%, with an overall average accuracy of 97.93%. In contrast, the single LSTM model achieved an average accuracy of 91.20%. In addition to the average accuracy, the maximum and minimum accuracies achieved across the clusters were compared between the two models. For the MC-LSTM model, the maximum accuracy was 99.24% and the minimum accuracy was 84.51%. The corresponding values for the single LSTM model were 92.42% and 82.03%, respectively.  
Figure 5: Clustered power consumption data from the target building, categorized into four seasonal and work condition-based clusters.  
The total computational complexity of both models was evaluated in terms of floating-point operations per second (FLOPs), providing a precise measurement of the computational workload [46; 47]. In LSTM cells, the primary computational burden arises from the matrix multiplication operations performed at each time step in each gate. Since each LSTM cell comprises four gates, the FLOPs per time step for a single LSTM layer \(l\) can be expressed as  
\[\text{FLOPs}^{(l)}_{\text{LSTM}}=T\times 4\times\left(I^{(l)}\times h^{(l)}+ \left(h^{(l)}\right)^{2}\right) \tag{9}\]  
where  
* \(T\) represents the sequence length (i.e., the number of time steps);
* \(I^{(l)}\) denotes the input dimension for the \(l\)-th LSTM layer (with \(I^{(1)}\) equal to the number of input features for the first layer and \(I^{(l)}=h^{(l-1)}\) for subsequent layers);
* \(h^{(l)}\) is the hidden state dimension of the \(l\)-th layer.  
Summing the overall layers, the total computational complexity for an LSTM model is given by  
\[\text{Total FLOPs}=\sum_{l=1}^{L}\text{FLOPs}^{(l)}_{\text{LSTM}} \tag{10}\]  
where \(L\) is the total number of LSTM layers. In our experiments, with \(T=10\), the input feature size \(I^{(1)}=14\), the hidden state dimension \(h=6\), and three layers, both models required 10.56 kFLOPs. Although the MC-LSTM model incorporates identical LSTM models for each cluster--resulting in a total model size that is four times larger--only one model is selected for execution during prediction, ensuring that the computational  
Figure 6: Comparison of actual and predicted power consumption data for the target building from 1 June 2023 to 31 May 2024.  
complexity remains equivalent to that of the single LSTM. This observation reinforces the robustness of the MC-LSTM approach for real-time energy management. For an intuitive comparison of performance metrics, these results are tabulated in Table 1.  
The relatively stable and predictable energy consumption patterns at the company headquarters contributed to minimal deviations between predicted and actual values, with no significant anomalies observed.