Prediksi Konsumsi Energi Termal Bangunan Menggunakan EPO-CatBoost - Mutiara Fadhilatuzzahro, (Prosiding Seminar Nasional Informatika Bela Negara (SANTIKA), 2025)
Building energy consumption is one of the main contributors to global energy use and carbon emissions in the construction sector. The increasing demand for heating and cooling in modern buildings requires an accurate prediction system to estimate thermal energy consumption from the design stage. An accurate prediction model can support energy efficiency policies. This study aims to develop a predictive model for building thermal energy consumption by optimizing the CatBoost algorithm using the Emperor Penguin Optimizer (EPO). The research was carried out through several stages, including data preprocessing and data splitting into two scenarios (70:30 and 80:20). The results show that the application of the EPO algorithm consistently improves the performance of the CatBoost model. In the 70:30 data split, the RMSE value for the Heating Load target decreased from 0.3522 to 0.3306 and for the Cooling Load from 0.7007 to 0.6239, while in the 80:20 data split, the RMSE decreased from 0.3352 to 0.3100 for Heating Load and from 0.6747 to 0.5589 for Cooling Load. It can be concluded that hyperparameter optimization using the Emperor Penguin Optimizer (EPO) significantly enhances the predictive performance of the CatBoost model and can serve as an effective alternative for estimating building thermal energy consumption, supporting the development of energy-efficient building design.