Makine Öğrenimi Algoritmaları Kullanılarak Bina Enerji Verimliliğinin Doğrusal Regresyon Analizi

Yıl 2024, Cilt: 9 Sayı: 2, 108 - 127, 27.12.2024

Hakan Yüksel

Öz

İklim değişikliği günümüz dünyasında kritik bir sorun haline gelmiştir. Günümüzde binalar, küresel enerjinin büyük çoğunluğunu kullandığından, bina enerji verimliliği (ısıtma yükü ve soğutma yükü) düzenlemeleri giderek daha katı hale gelmektedir. Binaların, enerji tüketimini anlamak ve bina tasarım aşamasında enerji yükünün iyi bir tahminine sahip olmak ise oldukça önemlidir. Geleneksel enerji tahmin çözümleri zaman alıcı ve maliyetli iken, makine öğrenimi algoritmaları bu duruma alternatif bir yaklaşım sunabilmektedir. Bu çalışmada, binaların ısıtma ve soğutma yüklerini tahmin etmek için bir lineer regresyon modeli kullanılmıştır. Veri modeli için enerji verimliliği veri seti kullanılarak enerji verimliliğinin değerlendirilmesi gerçekleştirilmiştir.

Anahtar Kelimeler

Makine öğrenmesi, Doğrusal regresyon analizi, Bina verimliliği

Kaynakça

• [1] Hong, T., Langevin, J., & Sun, K. (2018, October). Building simulation: Ten challenges. In Building simulation (Vol. 11, pp. 871-898). Springer Berlin Heidelberg.
• [2] Zhao, H. X., & Magoulès, F. (2012). A review on the prediction of building energy consumption. Renewable and Sustainable Energy Reviews, 16(6), 3586-3592.
• [3] Reyna, J. L., & Chester, M. V. (2015). The growth of urban building stock: Unintended lock‐in and embedded environmental effects. Journal of Industrial Ecology, 19(4), 524-537.
• [4] Foucquier, A., Robert, S., Suard, F., Stéphan, L., & Jay, A. (2013). State of the art in building modelling and energy performances prediction: A review. Renewable and Sustainable Energy Reviews, 23, 272-288.
• [5] Zhang, L., Wen, J., Li, Y., Chen, J., Ye, Y., Fu, Y., & Livingood, W. (2021). A review of machine learning in building load prediction. Applied Energy, 285, 116452.
• [6] Egwim, C. N., Alaka, H., Egunjobi, O. O., Gomes, A., & Mporas, I. (2024). Comparison of machine learning algorithms for evaluating building energy efficiency using big data analytics. Journal of Engineering, Design and Technology, 22(4), 1325-1350.
• [7] Forootan, M. M., Larki, I., Zahedi, R., & Ahmadi, A. (2022). Machine learning and deep learning in energy systems: A review. Sustainability, 14(8), 4832.
• [8] Yu, Z., Haghighat, F., Fung, B. C., & Yoshino, H. (2010). A decision tree method for building energy demand modeling. Energy and Buildings, 42(10), 1637-1646.
• [9] Amasyali, K., & El-Gohary, N. M. (2018). A review of data-driven building energy consumption prediction studies. Renewable and Sustainable Energy Reviews, 81, 1192-1205.
• [10] Gonzalez, P. A., & Zamarreno, J. M. (2005). Prediction of hourly energy consumption in buildings based on a feedback artificial neural network. Energy and buildings, 37(6), 595-601.
• [11] Dong, B., Cao, C., & Lee, S. E. (2005). Applying support vector machines to predict building energy consumption in tropical region. Energy and Buildings, 37(5), 545-553.
• [12] Ekici, B. B., & Aksoy, U. T. (2009). Prediction of building energy consumption by using artificial neural networks. Advances in Engineering Software, 40(5), 356-362.
• [13] Liang, X., Hong, T., & Shen, G. Q. (2016). Improving the accuracy of energy baseline models for commercial buildings with occupancy data. Applied energy, 179, 247-260.
• [14] Mocanu, E., Nguyen, P. H., Gibescu, M., & Kling, W. L. (2016). Deep learning for estimating building energy consumption. Sustainable Energy, Grids and Networks, 6, 91-99.
• [15] Reyna, J. L., & Chester, M. V. (2015). The growth of urban building stock: Unintended lock‐in and embedded environmental effects. Journal of Industrial Ecology, 19(4), 524-537.
• [16] Wei, Y., Zhang, X., Shi, Y., Xia, L., Pan, S., Wu, J., ... & Zhao, X. (2018). A review of data-driven approaches for prediction and classification of building energy consumption. Renewable and Sustainable Energy Reviews, 82, 1027-1047.
• [17] Ciulla, G., & D'Amico, A. (2019). Building energy performance forecasting: A multiple linear regression approach. Applied Energy, 253, 113500.
• [18] Yang, S., Wan, M. P., Chen, W., Ng, B. F., & Dubey, S. (2020). Model predictive control with adaptive machine-learning-based model for building energy efficiency and comfort optimization. Applied Energy, 271, 115147.
• [19] https://www.kaggle.com/datasets/ujjwalchowdhury/energy-efficiency-data-set/data
• [20] James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112, p. 18). New York: springer.
• [21] Bishop, C. M., & Nasrabadi, N. M. (2006). Pattern recognition and machine learning (Vol. 4, No. 4, p. 738). New York: springer.
• [22] Hastie, T. (2009). The elements of statistical learning: data mining, inference, and prediction.