Research Article
Year 2023,
Volume: 6 Issue: 2, 53 - 64, 30.12.2023
Abstract
Akıllı binaların sayısındaki artışın ve teknolojideki ilerlemenin bir sonucu olarak binalardaki enerji tüketimi giderek daha önemli bir hale gelmeye başlamıştır. Binalardaki enerji tüketimi tahmini, enerji verimliliği açısından kritik bir öneme sahiptir. Bu çalışmada, binalardaki enerji verimliliğini artırmak amacıyla Python programlama dilinde bulunan scikit-learn kütüphanesindeki çeşitli regresyon modelleri kullanıldı. Çalışmada, eğitim için hazır hale getirilmiş olan 768 satır ve 8 özellik içeren bir veri kümesi kullanıldı. Veriler önce normalizasyon işlemine tabi tutularak eğitime uygun hale getirildi. Çalışma, kütüphanede bulunan tüm modellerin varsayılan parametre değerleri kullanılarak eğitilmesiyle başladı. Daha sonra, bu modellerin performansları arasından MSE değeri 1,5 altında olan modeller belirlendi. Bu modeller, daha fazla iyileştirme için hiper-parametre optimizasyonuna tabi tutuldu. Bunun sonucunda, CatBoost regresyonu ve Bayes-Rastgele Orman hiper-parametre optimizasyonunun bir arada kullanılması ile 0,997 R2 ve 0,23 MSE skorları bu çalışmadaki en yüksek başarı sonucu olarak elde edildi. Bu çalışma, akıllı binaların yaygınlaşmasıyla birlikte enerji verimliliğini artırmak için Makine Öğrenmesi tekniklerini ve hiper-parametre optimizasyonunu kullanmanın potansiyelini göstermektedir. Teknolojik ilerlemeye paralel olarak, bu tür çalışmalar inşaat ve mimari alanında daha yeşil, sürdürülebilir ve çevre dostu yapıların geleceği için umut vaat etmektedir.
References
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Year 2023,
Volume: 6 Issue: 2, 53 - 64, 30.12.2023
Abstract
References
- Goyal, M., Pandey, M., & Thakur, R. (2020, June). Exploratory analysis of machine learning techniques to predict energy efficiency in buildings. In 2020 8th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO) (pp. 1033-1037). IEEE.
- Al-Rakhami, M., Gumaei, A., Alsanad, A., Alamri, A., & Hassan, M. M. (2019). An ensemble learning approach for accurate energy load prediction in residential buildings. IEEE Access, 7, 48328-48338.
- Sajjad, M., Khan, S. U., Khan, N., Haq, I. U., Ullah, A., Lee, M. Y., & Baik, S. W. (2020). Towards efficient building designing: Heating and cooling load prediction via multi-output model. Sensors, 20(22), 6419.
- Tsanas, A., & Xifara, A. (2012). Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools. Energy and buildings, 49, 560-567.
- Moradzadeh, A., Mansour-Saatloo, A., Mohammadi-Ivatloo, B., & Anvari-Moghaddam, A. (2020). Performance evaluation of two machine learning techniques in heating and cooling loads forecasting of residential buildings. Applied Sciences, 10(11), 3829.
- Akgundogdu, A. (2020). Comparative analysis of regression learning methods for estimation of energy performance of residential structures. Erzincan University Journal of Science and Technology, 13(2), 600-608.
- Venkat Ramana Reddy, A., & Sudheer Kumar, M. (2020). A Comparative Analysis of Regression Algorithms for Energy Estimation in Residential Buildings. In ICICCT 2019–System Reliability, Quality Control, Safety, Maintenance and Management: Applications to Electrical, Electronics and Computer Science and Engineering (pp. 300-311). Springer Singapore.
- Pachauri, N., & Ahn, C. W. (2022, November). Regression tree ensemble learning-based prediction of the heating and cooling loads of residential buildings. In Building Simulation (Vol. 15, No. 11, pp. 2003-2017). Beijing: Tsinghua University Press.
- Gao, W., Alsarraf, J., Moayedi, H., Shahsavar, A., & Nguyen, H. (2019). Comprehensive preference learning and feature validity for designing energy-efficient residential buildings using machine learning paradigms. Applied Soft Computing, 84, 105748.
- Moayedi, H., Bui, D. T., Dounis, A., Lyu, Z., & Foong, L. K. (2019). Predicting heating load in energy efficient buildings through machine learning techniques. Applied Sciences, 9(20), 4338.
- Wu, J., Chen, X. Y., Zhang, H., Xiong, L. D., Lei, H., & Deng, S. H. (2019). Hyperparameter optimization for machine learning models based on Bayesian optimization. Journal of Electronic Science and Technology, 17(1), 26-40.
- Chen, T., & Guestrin, C. (2016, August). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining (pp. 785-794).
- Wang, Y., Pan, Z., Zheng, J., Qian, L., & Li, M. (2019). A hybrid ensemble method for pulsar candidate classification. Astrophysics and Space Science, 364, 1-13.
- Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (2018). CatBoost: unbiased boosting with categorical features. Advances in neural information processing systems, 31.
- Yenigün, O. (2022), Smart Aspects of CatBoost Algorithm, 25 Nisan 2023 tarihinde https://python.plainenglish.io/smart-aspects-of-catboost-algorithm-2720a6de4da6 adresinden alındı.
- Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., ... & Liu, T. Y. (2017). Lightgbm: A highly efficient gradient boosting decision tree. Advances in neural information processing systems, 30.
- Khandelwal, E. (2020), Which algorithm takes the crown: Light GBM vs XGBOOST?, 25 Nisan 2023 tarihinde www.analyticsvidhya.com/blog/2017/06/which-algorithm-takes-the-crown-light-gbm-vs-xgboost/ adresinden alındı.
There are 17 citations in total.
Details
| Primary Language | Turkish |
|---|---|
| Subjects | Information Systems (Other) |
| Journal Section | Articles |
| Authors | |
| Early Pub Date | December 29, 2023 |
| Publication Date | December 30, 2023 |
| Submission Date | October 24, 2023 |
| Acceptance Date | November 19, 2023 |
| Published in Issue | Year 2023 Volume: 6 Issue: 2 |
Uluborlu Journal of Vocational Sciences