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Evolusi : Jurnal Sains dan ManajemenEvolusi : Jurnal Sains dan Manajemen

Minimizing the use of cooling and heating devices in buildings can be attempted by considering the building materials used, so that the energy load that will be used can be calculated, as well as saving energy use. In this study, the author chose the XGBoost and linear regression methods to compare and determine the best methods in estimating the heating and cooling loads of buildings. The author divided the dataset into training data and testing data, 75% as training data and 25% as testing data, using 10-Fold Cross-Validation on training data. In the data processing process, the author uses Jupyter Notebook with the Python programming language. The results of the study using the linear regression method on heating and cooling loads, the author obtained an RMSE value of 3, while the author obtained the smallest RMSE value using the XGBoost method of 1 on heating and cooling loads.

Metode XGBoost menghasilkan nilai RMSE paling kecil, yaitu 1, dibandingkan dengan 3 pada regresi linier.Karena nilai RMSE yang lebih rendah, XGBoost lebih akurat dalam memprediksi beban pemanasan dan pendinginan bangunan.Oleh karena itu, XGBoost lebih direkomendasikan untuk estimasi beban energi bangunan daripada regresi linier.

Bagaimana perbandingan kinerja XGBoost dengan algoritma machine learning lain seperti CatBoost atau LightGBM dalam estimasi beban energi bangunan? Apakah integrasi data variabel lingkungan tambahan, seperti histori cuaca lokal dan kebijakan energi, dapat meningkatkan akurasi prediksi XGBoost? Bagaimana efektivitas penggunaan transfer learning pada model XGBoost ketika diterapkan pada dataset bangunan sejenis di wilayah geografis berbeda?.

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