Browsing by Author "Milani, Muhammed"

Now showing 1 - 2 of 2

A Comprehensive Method for Exploratory Data Analysis and Preprocessing the Ashrae Database for Machine Learning
(Pergamon-elsevier Science Ltd, 2025) Rahmanparast, Amir; Milani, Muhammed; Camci, Muhammet; Karakoyun, Yakup; Acikgoz, Ozgen; Dalkilic, Ahmet Selim
Thermal comfort prediction is crucial for building energy efficiency and occupant comfort. ML methods are commonly used to predict thermal comfort. This research presents a comprehensive process for exploring and preprocessing the ASHRAE Database, providing a substantial dataset comprising 107,583 records of thermal comfort observations to create ML algorithms that can estimate Fanger's PMV. With the most detailed cleaning and preprocessing stages in the literature, which included the imputation of missing values and the management of outliers, the final dataset is reduced to 55,443 records for the analyses. For practical applications and indoor comfort assessments, its estimation offers significant advantages due to its speed, ease of use, and costeffectiveness. This study aimed to investigate which parameters are important in Fanger's PMV model and which subset of variables is best for variable selection using different feature selection and analysis methods. The Ta and Tr had a high correlation value of 0.92, indicating a robust link between these two variables. The study employed Feature importance, the SelectKBest, SHAP, P-box, and PDP analyses, which showed consistency and suggested condensing the first six elements into three, and also was validated with the Chinese Database with 41,977 entries. The study targeted three parameters: Ta, clo, and M, using less expensive and simple measurement devices. To evaluate the accuracy of the research performance, RF and SVM models were created based on these three parameters. The results indicated that they have the accuracies of 85% and 70%, respectively, which are far better than the conventional models.
Parameter Reduction for PMV Prediction Via Data Driven Approaches Using the ASHRAE Global Thermal Comfort Database II and Chinese Dataset
(Pergamon-Elsevier Science Ltd, 2025) Rahmanparast, Amir; Milani, Muhammed; Camci, Muhammet; Karakoyun, Yakup; Dalkilic, Ahmet Selim
Thermal comfort significantly impacts building occupants' well-being, efficiency, and energy consumption. In this research, ASHRAE Thermal Comfort Database II, a comprehensive dataset consisting of 85,583 thermal comfort observation records, was used to develop ML models that can predict Fanger's PMV. While implementing the proposed model, feature selection analyses were used to indicate the importance of the parameters. The objective of the study is to reduce the factors to predict PMV from six (Tr, Va, Ta, clo, RH, and M) to three (Ta, clo, and M), keeping the predictions accurate and considering new, practical, and cost-effective aspects as the second work in the literature on the reduction of PMV factors having the higher accuracy. In the work, 10 ML techniques were evaluated, and the XGBoost model showed superior performance. The model attains remarkable outcomes for accuracy and interpretability by emphasizing critical characteristics: Ta, clo, and M. The research reveals that optimized version of XGBoost can improve the accuracy rate of PMV prediction to 96.29 % with 0.93 R2,0.124 MAE and 0.93 CV-R2 Mean values, while Lasso, the least accurate model among the 10 tested models, achieves an accuracy rate of 59.63 % with 0.22 R2, 0.478 MAE and 0.22 CV-R2 Mean values. The validation has been performed with various methods including cross-validation with ASHRAE Thermal Comfort Database II and fine-tuning using the Chinese Thermal Comfort Dataset containing 41,977 records. This work aims to increase the popularity of PMV usage, providing a practical and generalized solution suitable for a broad audience.