*4.1. Case I: Performance of the Proposed Model on Korea Power Company Buildings Dataset*

Our predictive analysis, first and foremost, begins with the electrical load dataset from KEPRI building and KEPCO dong-Daegu substation building for which historical data is available from 2015 to date, as shown in Figures 14 and 15 respectively. Based on the available accumulated historical demand load and weather information data, a prediction model was developed. We estimated the prediction accuracy by predicting demand loads of 2019, considering both the MAPE and RMSE indices.

**Figure 14.** Korean research institute (KEPRI) demand load profile.

**Figure 15.** Korean Electric Power Company (KEPCO) substation demand load profile.

The first stage of the analysis was to analyze the scenarios of two datasets (i.e., KEPRI and KEPCO) of different feature characteristics. These datasets contain demand load and feature data at a 15-minute sample resolution. The datasets cover the period of five years from September 2015. A four-year dataset was used to train the predictive model, whereas a one-year dataset was used for testing and cross-validation. The hyperparameters were tuned to compensate for error correction using the seven days prior to the starting date. Specific days among the four seasons were selected for load prediction. Figures 16 and 17 show the stochastic forecast results of the ensemble prediction for the selected days in each season of the two datasets above.

**Figure 16.** KEPRI stochastic demand forecast. (**a**) Spring, (**b**) Summer, (**c**) Fall, (**d**) Winter.

**Figure 17.** KEPCO substation stochastic demand forecast. (**a**) Spring, (**b**) Summer, (**c**) Fall, (**d**) Winter.

From the result, it is imperative to know that all seasons take the shape and form of the actual measured load profile. In most cases, the actual measured values lie within the minimum and maximum confidence interval. Before reporting the performance of the ensemble model obtained by combining multiple individual models, we first analyzed the performance of the parametric models mentioned in Section 3. With the same dataset for both training and validation, we estimated the demand load forecast with ANN and K-means separately without the ensemble effect.

To yield credible results, we trained each model multiple times and averaged the losses. From the prediction error results, as shown in Figure 18 and Table 4, it is observed in the figures that the ensemble model can improve the performance of the model, with the error correction model implementation. For simplicity, the ensemble model consisted of only two parametric models, but the framework is scalable for multiple models.

**Figure 18.** Performance evaluation of prediction models. (**a**) KEPRI data set, (**b**) KEPCO dataset.


**Table 4.** Prediction accuracy of forecasting dataset.
