Cooling load prediction of a double-story terrace house using ensemble learning techniques and genetic programming with SHAP approach (

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dc.contributor.authorÇakıroğlu, Celal
dc.contributor.authorAydın, Yaren
dc.contributor.authorBekdaş, Gebrail
dc.contributor.authorIşıkdağ, Ümit
dc.contributor.authorSadeghifam, Aidin Nobahar
dc.contributor.authorAbualigah, Laith
dc.date.accessioned2024-12-26T18:53:20Z
dc.date.available2024-12-26T18:53:20Z
dc.date.issued2024
dc.departmentTAÜ, Mühendislik Fakültesi, İnşaat Mühendisliği Bölümüen_US
dc.description.abstractSince the cooling systems used in buildings in hot climates account for a significant portion of the energy consumption, it is very important for both economy and environment to accurately predict the cooling load and consider it in building designs. This study aimed to maximize energy efficiency by appropriately selecting the features of a building that affect its cooling load. To this end, data-driven, accurate, and accessible tools were developed that enable the prediction of the cooling load of a building by practitioners. The study involves simulating the energy consumption of a mid-rise, double-story terrace house in Malaysia using building information modeling (BIM) and estimating the cooling load using ensemble machine learning models and genetic programming. Categorical Boosting (CatBoost), eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Random Forest (RF) models have been developed and made available as an online interactive graphical user interface on the Streamlit platform. Furthermore, the symbolic regression technique has been utilized to obtain a closed-form equation that predicts the cooling load. The dataset used for training the predictive models comprised 94,310 data points with 10 input variables and the cooling load as the output variable. Performance metrics such as the coefficient of determination (R2), root mean squared error (RMSE), and mean absolute error (MAE) were used to measure the predictive model performances. The results of the machine learning models indicated successful prediction, with the CatBoost model achieving the highest score (R2 = 0.9990) among the four ensemble models and the predictive equation. The SHAP analysis determined the aspect ratio of the building as the most impactful feature of the building.
dc.identifier.citationÇakıroğlu, C., Aydın, Y., Bekdaş, G., Işıkdağ, Ü., Sadeghifam, Aidin N., Abualigah, L. (2024). Cooling load prediction of a double-story terrace house using ensemble learning techniques and genetic programming with SHAP approach. Energy & Buildings, 315.
dc.identifier.doi10.1016/j.enbuild.2024.114329
dc.identifier.scopus2-s2.0-85194057528
dc.identifier.urihttps://hdl.handle.net/20.500.12846/1502
dc.identifier.volume315en_US
dc.identifier.wosWOS:001251633700001
dc.indekslendigikaynakScopus
dc.language.isoen
dc.relation.ispartofEnergy & Buildings
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.titleCooling load prediction of a double-story terrace house using ensemble learning techniques and genetic programming with SHAP approach (
dc.typeArticle

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