**7. Conclusions**

In this study, we proposed a novel 2-stage STLF model that combines popular STLF models by using a DNN to further expand the domain of applicability. In the first stage, we used XGBoost and RF algorithms to predict day-ahead electric energy consumption. In the second stage, we built a load forecasting model based on DNN by using the forecasted results of XGBoost and RF and other external data as new input variables. To verify the forecasting performance of our proposed model, we performed day-ahead forecasting using actual factory electric energy consumption data and compared its accuracy with several machine learning methods and our previous forecasting models. The comparison showed that our proposed model showed the best prediction performance in terms of CVRMSE and MAPE.

Additionally, to show the applicability of our model, we performed CCHP operation scheduling based on forecasting and economic analysis, decided the best electric rate and contract demand, and showed how much could be saved by the decision. According to the experiment, the electric cost was reduced by 37% annually.

**Author Contributions:** All authors have read and agreed to the published version of the manuscript. Conceptualization, S.P. and J.M.; methodology, S.P. and J.M.; software, S.J.; validation, S.P., J.M. and S.J.; formal analysis, S.R. and E.H.; data curation, S.R.; writing—original draft preparation, S.P. and E.H.; writing—review and editing, E.H. and S.W.B.; visualization, S.J.; supervision, E.H.; project administration, E.H. and S.W.B.; funding acquisition, S.R.

**Funding:** This research was supported in part by the Korea Electric Power Corporation (grant number: R18XA05) and in part by Energy Cloud R&D Program (grant number: 2019M3F2A1073179) through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT.

**Conflicts of Interest:** The authors declare no conflict of interest.
