Seleksi Fitur Berdasarkan Korelasi Pearson dalam Pemodelan Efisiensi Energi Bangunan
Desmarita Leni, Aggrivina Dwiharzandis, Ruzita Sumiati, Haris Haris, Sicilia Afriyani
Abstract
Prediksi beban pemanasan dan pendinginan bangunan merupakan langkah penting untuk perencanaan dan pengelolaan sistem energi. Hal ini, tidak terlepas dari berkontribusi beban pemanasan dan pendinginan bangunan yang menyumbang 30% dari total konsumsi energi global. Penelitian ini bertujuan untuk menerapkan metode seleksi fitur berdasarkan korelasi Pearson dalam pemodelan prediksi beban pemanasan dan pendinginan bangunan menggunakan Artificial Neural Network (ANN). Korelasi Pearson digunakan untuk menganalisis hubungan antara variabel input dan variabel target. Fitur-fitur yang memiliki korelasi signifikan dengan variabel target digunakan sebagai dataset untuk pelatihan model, sedangkan yang tidak memiliki korelasi signifikan dihapus dari dataset pelatihan. Evaluasi dilakukan menggunakan metrik evaluasi seperti Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), dan R-squared untuk mengukur tingkat keakuratan dan kinerja model dalam memprediksi beban pemanasan dan pendinginan. Hasil pemodelan menunjukkan bahwa seleksi fitur berdasarkan korelasi Pearson menghasilkan prediksi yang sangat akurat untuk beban pemanasan dan pendinginan bangunan. Model ini menunjukkan kinerja yang baik selama pelatihan dan validasi dengan Cross Validation (CV) menggunakan k = 10. Hasil evaluasi model diperoleh nilai MAE 0.457, RMSE 0.628, dan R-squared 0.996 untuk beban pemanasan, sedangkan untuk beban pendinginan diperoleh nilai MAE sebesar 1.163, RMSE 1.74, dan R-squared 0.967. Hasil ini mengindikasikan bahwa seleksi fitur dengan korelasi Pearson dapat dijadikan pendekatan yang efektif untuk meningkatkan performa model prediksi menggunakan machine learning, terutama dalam konteks prediksi beban pemanasan dan pendinginan bangunan.
Keywords
Artificial Neural Network; Energi; Fitur Pemodelan; Korelasi Pearson; Seleksi
References
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DOI: https://doi.org/10.24967/teksis.v8i2.2525
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Desmarita Leni, Aggrivina Dwiharzandis, Ruzita Sumiati, Haris Haris, Sicilia AfriyaniAbstract
Keywords
References
J.-S. Chou and D.-K. Bui, “Modeling heating and cooling loads by artificial intelligence for energy-efficient building design,†Energy Build., vol. 82, pp. 437–446, Oct. 2014, doi: 10.1016/j.enbuild.2014.07.036.
B. Chenari, J. Dias Carrilho, and M. Gameiro da Silva, “Towards sustainable, energy-efficient and healthy ventilation strategies in buildings: A review,†Renew. Sustain. Energy Rev., vol. 59, pp. 1426–1447, Jun. 2016, doi: 10.1016/j.rser.2016.01.074.
A. Saleh, “Design Of Energy-Efficient Buildings And Benefits Of Enhancing Reliance On Natural Lighting Through Maximizing The External Reflected Component,†J. Al-Azhar Univ. Eng. Sect., vol. 11, no. 41, pp. 1321–1325, Oct. 2016, doi: 10.21608/auej.2016.19312.
Watson, Boudreau, and Chen, “Information Systems and Environmentally Sustainable Development: Energy Informatics and New Directions for the IS Community,†MIS Q., vol. 34, no. 1, p. 23, 2010, doi: 10.2307/20721413.
B. Huang, X. Bai, Z. Zhou, Q. Cui, D. Zhu, and R. Hu, “Energy informatics: Fundamentals and standardization,†ICT Express, vol. 3, no. 2, pp. 76–80, Jun. 2017, doi: 10.1016/j.icte.2017.05.006.
A. Tsanas and A. Xifara, “Accurate quantitative estimation of energy performance of residential buildings using statistical machine learning tools,†Energy Build., vol. 49, pp. 560–567, Jun. 2012, doi: 10.1016/j.enbuild.2012.03.003.
Z. Zhang, “A gentle introduction to artificial neural networks,†Ann. Transl. Med., vol. 4, no. 19, pp. 370–370, Oct. 2016, doi: 10.21037/atm.2016.06.20.
V. V. Mokeev, “Prediction of Heating Load and Cooling Load of Buildings Using Neural Network,†in 2019 International Ural Conference on Electrical Power Engineering (UralCon), IEEE, Oct. 2019, pp. 417–421. doi: 10.1109/URALCON.2019.8877655.
D. Leni, M. Muchlisinalahuddin, M. Maimuzar, H. Haris, and H. Hendra, “Analisis Heatmap Korelasi dan Scatterplot untuk Mengidentifikasi Faktor-Faktor yang Mempengaruhi Pelabelan AC efisiensi Energi,†J. Rekayasa Mater. Manufaktur dan Energi, vol. 6, no. 1, Mar. 2023, doi: 10.30596/rmme.v6i1.13133.
J. Hegde and B. Rokseth, “Applications of machine learning methods for engineering risk assessment – A review,†Saf. Sci., vol. 122, p. 104492, Feb. 2020, doi: 10.1016/j.ssci.2019.09.015.
P. Ghosh et al., “Efficient Prediction of Cardiovascular Disease Using Machine Learning Algorithms With Relief and LASSO Feature Selection Techniques,†IEEE Access, vol. 9, pp. 19304–19326, 2021, doi: 10.1109/ACCESS.2021.3053759.
M. Ali and T. Aittokallio, “Machine learning and feature selection for drug response prediction in precision oncology applications,†Biophys. Rev., vol. 11, no. 1, pp. 31–39, Feb. 2019, doi: 10.1007/s12551-018-0446-z.
A. Adriansyah, D. Leni, and R. Sumiati, “Comparative analysis of energy-efficient air conditioner based on brand,†J. Polimesin, vol. 21, no. 04, Aug. 2023, doi: 10.30811/jpl.v21i4.3625.
D. Leni, “Prediksi Pelabelan Rating AC Efisiensi Energi Menggunakan Pemodelan Machine Learning,†J. Ilm. Momentum, vol. 19, no. 1, p. 12, May 2023, doi: 10.36499/jim.v19i1.7832.
Y. Sugianela and T. Ahmad, “Pearson Correlation Attribute Evaluation-based Feature Selection for Intrusion Detection System,†in 2020 International Conference on Smart Technology and Applications (ICoSTA), IEEE, Feb. 2020, pp. 1–5. doi: 10.1109/ICoSTA48221.2020.1570613717.
E. Resch, R. A. Bohne, T. Kvamsdal, and J. Lohne, “Impact of Urban Density and Building Height on Energy Use in Cities,†Energy Procedia, vol. 96, pp. 800–814, Sep. 2016, doi: 10.1016/j.egypro.2016.09.142.
S. Papadopoulos, E. Azar, W.-L. Woon, and C. E. Kontokosta, “Evaluation of tree-based ensemble learning algorithms for building energy performance estimation,†J. Build. Perform. Simul., vol. 11, no. 3, pp. 322–332, May 2018, doi: 10.1080/19401493.2017.1354919.
D. Leni, “Pemilihan Algoritma Machine Learning Yang Optimal Untuk Prediksi Sifat Mekanik Aluminium,†J. EngineEnergi, Manufaktur, dan Mater., vol. 7, no. 1, pp. 35–44, 2023.
A. Mosavi, M. Salimi, S. Faizollahzadeh Ardabili, T. Rabczuk, S. Shamshirband, and A. Varkonyi-Koczy, “State of the Art of Machine Learning Models in Energy Systems, a Systematic Review,†Energies, vol. 12, no. 7, p. 1301, Apr. 2019, doi: 10.3390/en12071301.
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