*4.2. Case II: Performance of the Proposed Model on Testbed Dataset*

The second scenario of the case studies was to examine the adaptability of the proposed model. To this end, we use the hyper-parameters of the ensemble model tuned with the testbed dataset to train load forecasting models for different load form forecasts. Here, the load profile of the dataset does not follow any regular pattern, unlike in Case I. The testbed platform gathers information from a living laboratory, which consists of research laboratories, teaching classrooms, and faculty offices. The activities in these places are not routine; hence, the load profile does not follow a regular pattern, as shown in Figure 19.

**Figure 19.** Testbed demand load profile.

For the proposed ensemble model, the training period was from June 2015 to December 2018. Similar to Case I, neural network and K-means parametric models, together with the proposed ensemble model, were used to train demand data independently from 2015 to May 2019, while the data from June 2019 to December 2019 was used for the forecast. Figures 20 and 21 show the results of the stochastic forecast and prediction accuracy, respectively. It is seen in the figures that the proposed ensemble model has the lowest overall MAPE and RMSE among the predictive models.

**Figure 20.** Testbed stochastic demand load forecast. (**a**) Spring, (**b**) Summer, (**c**) Fall, (**d**) Winter.

**Figure 21.** Testbed dataset performance evaluation of prediction models. (**a**) Performance estimation with MAPE, (**b**) Performance estimation with RMSE.

For all three prediction models, we repeated the trials for randomly selected days and estimated the average MAPE and RMSE. As shown in Figure 21 and Table 5, the ensemble strategy dramatically reduces the losses of MAPE and RMSE, especially for different load profiles with no regular pattern. It is, therefore, reasonable to conclude that the proposed model is robust even for demand load with many irregularities in load patterns. It is also observed that the ensemble strategy can reduce the deviation of multiple trials. This also indicates the higher adaptive capability of the proposed model with the ensemble strategy.


**Table 5.** Prediction accuracy of forecasting testbed dataset.
