Yazar "Abualigah, Laith" seçeneğine göre listele

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    Cooling load prediction of a double-story terrace house using ensemble learning techniques and genetic programming with SHAP approach
    (Elsevier Science Sa, 2024) Cakiroglu, Celal; Aydin, Yaren; Bekdas, Gebrail; Isikdag, Umit; Sadeghifam, Aidin Nobahar; Abualigah, Laith
    Since 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.
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    Öğe
    Cooling load prediction of a double-story terrace house using ensemble learning techniques and genetic programming with SHAP approach (
    (2024) Çakıroğlu, Celal; Aydın, Yaren; Bekdaş, Gebrail; Işıkdağ, Ümit; Sadeghifam, Aidin Nobahar; Abualigah, Laith
    Since 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.
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    Öğe
    Data-driven interpretable ensemble learning methods for the prediction of wind turbine power incorporating SHAP analysis
    (Elsevier, 2023) Çakıroğlu, Celal; Demir, Sercan; Özdemir, Mehmet Hakan; Aylak, Batin Latif; Sariisik, Gencay; Abualigah, Laith
    Wind energy increasingly attracts investment from many countries as a clean and renewable energy source. Since wind energy investment cost is high, the efficiency of a potential wind power plant should be determined using wind power prediction models and wind speed data before installation. Accurate wind power estimation is crucial to set up comprehensive strategies for wind power generation. This study estimated the power produced in a wind turbine using six different regression algorithms based on machine learning using temperature, humidity, pressure, air density, and wind speed data. The proposed estimation model was evaluated on the data received between 2011 and 2020 at station 17,112 in Çanakkale, Turkey. XGBoost, Random Forest, LightGBM, CatBoost, AdaBoost, and M5-Prime algorithms were used to create predictive models. Furthermore, model explanations were presented using the SHAP methodology. Among the regression algorithms evaluated according to the R2 performance metric, the best performance was obtained from the XGBoost algorithm. Regarding computational speed, the LightGBM model emerged as the most efficient model. The wind speed was