WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

Permanent URI for this collectionhttps://hdl.handle.net/11147/7150

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Access Right: Closed AccessAuthor: search.filters.author.Gokalp, Osman

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  • Article
    Prediction of HVAC Operational Variables Using Recurrent Neural Networks for Advanced Control Strategies
    (Elsevier, 2025) Erdem, Merve Kuru; Gokalp, Osman; Calis, Gulben
    Heating, Ventilation, and Air Conditioning (HVAC) systems are major energy consumers in buildings, making accurate prediction of their operational variables essential for improving energy efficiency and occupant comfort. This study compares three recurrent neural network (RNN) architectures, namely standard RNN, Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) for multi-input, multi-output prediction of nine HVAC variables using high-resolution data from a real office building (2018-2020). A two-stage hyperparameter optimization varying network depth, width, learning rate, and batch size was applied. Results show that a shallow architecture with a single hidden layer of 16 nodes, learning rate of 0.001, and batch size of 64 offers the best balance between accuracy and generalization. While the standard RNN showed higher errors and lower stability, both LSTM and GRU performed well. The GRU achieved the lowest average rank (4.2) and mean validation loss (0.0049) across cross-validation folds, demonstrating superior consistency and reliability, and was selected for final evaluation. On the holdout test set, GRU consistently outperformed the other models, achieving R-2 > 0.81 for seven of nine variables, with a peak R-2 of 0.99 for mean indoor air temperature and an overall average R-2 of 0.80. These results highlight GRU's advantages in accuracy, stability, and efficiency, suggesting its suitability for intelligent building control. The study's scope is limited to three RNNbased architectures with a constrained hyperparameter search; future work should extend comparisons to broader model families and datasets.
  • Conference Object
    Performance Evaluation of Filter-Based Gene Selection Methods in Cancer Classification
    (IEEE, 2025) Gokalp, Osman
    With the advances in microarray technology, gene expression levels can be measured efficiently, and this data can be used to solve important problems such as cancer classification. However, microarray data suffers from the high-dimensionality problem and requires dimensionality reduction techniques such as feature selection. This study addresses the cancer classification problem using microarray datasets and comparatively evaluates the performance of different filter-based gene (feature) selection methods. To this end, 11 microarray datasets have been evaluated using 6 different filter methods, and experimental results are presented. According to the findings, the gene selection methods used can improve classification performance by 5% to 30%. Using 5-fold cross-validation, the highest accuracy rates were achieved with 32 genes selected by the gain ratio filter for the Breast and Colon datasets, and with 8 genes selected by the information gain filter for the CNS dataset.