**4. Case Study and Scenario Analysis**

In this section, we show the performance of the proposed framework. In the first part, we test the algorithm on the Korean Electric Power Company (KEPCO) load consumption data from the Korean research institute (KEPRI) building and substation. The load data and weather information used were acquired from iSmart [44] and K-weather [45], respectively. We compare our forecast results with parametric model forecasts. In the second part, we test the algorithm on a set of generalized testbed data of different load sizes, shapes, and characteristics. Smart energy meters and IoT sensors were used to gather load data and feature data for the prediction analysis. Prediction analysis for four seasons—spring, summer, fall, and winter—was conducted in each case study. The optimal values estimated were used to predict randomly selected days for each season. In this study, the average model training computational time and predictive time are specified in Table 3. All the data processing and modeling tasks were implemented using MATLAB software (2019a, MathsWorks, Natick, MA, USA) on a 64-bit Intel i7 (4 CPUs and 16 GB RAM) with Windows operating system. The proposed model is relatively computationally non-intensive for both online and offline load forecasts. However, the training computation time could be reduced with parallel processing.

**Table 3.** The computation time of processes.

