**5. Conclusions**

This paper proposes an ensemble prediction framework for stochastic demand load forecasting to take advantage of the diversities in forecasting models. The ensemble problem is formulated as a two-stage random forest problem with a series of homogenous prediction models. A heuristic trained model combiner, together with an error correction model, enable the proposed model to have high accuracy as well as satisfactory adaptive capabilities. KEPRI building, KEPCO substation building, and testbed datasets were used to verify the effectiveness of the proposed model with different case scenarios. The comparisons with existing parametric models show that the proposed model is superior in both forecasting accuracy and robustness in load profile variation. The results from the case study analysis show that the proposed ensemble method effectively improves the forecasting performance when compared with individual models. For simplicity, two parametric models(i.e., K-means with Bayesian and ANN models) were adapted in this analysis. However, in future work, other deep learning models, such as a recurrent neural network (RNN) and long short-term memory (LSTM) neural network, can be seamlessly incorporated into the model to enhance its performance.

**Author Contributions:** Conceptualization, S.H. and K.A.A.; Methodology, K.A.A.; Software, K.A.A.; Validation, S.P., and G.K. Formal Analysis, K.A.A., G.K., S.P. and H.J.; Investigation, G.K., H.J. and S.P.; Resources, S.H.; Data Curation, K.A.A. and G.K. and S.P.; Writing-Original Draft Preparation, K.A.A.; Writing-Review & Editing, K.A.A.; Visualization, K.A.A. and G.K.; Supervision, S.H.; Project Administration, S.H.; Funding Acquisition, S.H. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by Korea Institute of Energy Technology Evaluation and Planning(KETEP) and the Ministry of Trade, Industry and Energy(MOTIE) grant number [20182010600390].

**Conflicts of Interest:** The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.
