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Lembaga Penelitian Pengabdian Masyarakat Universitas Nusa Mandiri
Creation is distributed below Lisensi Creative Commons Atribusi-NonKomersial 4.0 Internasional.
PEMODELAN PREDIKTIF KONSUMSI ENERGI BANGUNAN GEDUNG KOMERSIAL DENGAN ALGORITMA SUPPORT VECTOR MACHINE
Keywords:
SMOreg, LibSVM, Support Vector Machine, Prediksi Total Konsumsi Energi.
Abstract
Konsumsi energi bangunan yang semakin meningkat mendorong para peneliti untuk membangun sebuah model prediksi dengan menerapkan metode machine learning, namun masih belum diketahui model yang paling akurat. Model prediktif untuk konsumsi energi bangunan komersial penting untuk konservasi energi. Dengan menggunakan model yang tepat, kita dapat membuat desain bangunan yang lebih efisien dalam penggunaan energi. Dalam tulisan ini, kami mengusulkan model prediktif berdasarkan metode pembelajaran mesin untuk mendapatkan model terbaik dalam memprediksi total konsumsi energi. Algoritma yang digunakan yaitu SMOreg dan LibSVM dari kelas Support Vector Machine, kemudian untuk evaluasi model berdasarkan nilai Mean Absolute Error dan Root Mean Square Error. Dengan menggunakan dataset publik yang tersedia, kami mengembangkan model berdasarkan pada mesin vektor pendukung untuk regresi. Hasil pengujian kedua algoritma tersebut diketahui bahwa algoritma SMOreg memiliki akurasi lebih baik karena memiliki nilai MAE dan RMSE sebesar 4,70 dan 10,15, sedangkan untuk model LibSVM memiliki nilai MAE dan RMSE sebesar 9,37 dan 14,45. Kami mengusulkan metode berdasarkan algoritma SMOreg karena kinerjanya lebih baik.
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References
Ahmad, A. S., Hassan, M. Y., Abdullah, M. P., Rahman, H. A., Hussin, F., Abdullah, H., & Saidur, R. (2014). A review on applications of ANN and SVM for building electrical energy consumption forecasting. Renewable and Sustainable Energy Reviews, 33, 102–109. https://doi.org/10.1016/j.rser.2014.01.069
Colanus, I., Drajana, R., & Selection, F. (2017). METODE SUPPORT VECTOR MACHINE DAN FORWARD SELECTION PREDIKSI PEMBAYARAN PEMBELIAN BAHAN BAKU, 9, 116–123.
Deng, H., Fannon, D., & Eckelman, M. J. (2018). Predictive modeling for US commercial building energy use: A comparison of existing statistical and machine learning algorithms using CBECS microdata. Energy and Buildings, 163, 34–43. https://doi.org/10.1016/J.ENBUILD.2017.12.031
Indriyanti, I., & Subekti, A. (2018). Laporan Akhir Penelitian “Pemodelan Prediktif Konsumsi Energi Bangunan Gedung Komersial Dengan Algoritma Support Vector Machine.” Jakarta.
Michaels, J. (2016). 2012 Commercial Buildings Energy Consumption Survey: Energy Usage Summary. Washington, DC. Retrieved from https://www.eia.gov/consumption/commercial/
Colanus, I., Drajana, R., & Selection, F. (2017). METODE SUPPORT VECTOR MACHINE DAN FORWARD SELECTION PREDIKSI PEMBAYARAN PEMBELIAN BAHAN BAKU, 9, 116–123.
Deng, H., Fannon, D., & Eckelman, M. J. (2018). Predictive modeling for US commercial building energy use: A comparison of existing statistical and machine learning algorithms using CBECS microdata. Energy and Buildings, 163, 34–43. https://doi.org/10.1016/J.ENBUILD.2017.12.031
Indriyanti, I., & Subekti, A. (2018). Laporan Akhir Penelitian “Pemodelan Prediktif Konsumsi Energi Bangunan Gedung Komersial Dengan Algoritma Support Vector Machine.” Jakarta.
Michaels, J. (2016). 2012 Commercial Buildings Energy Consumption Survey: Energy Usage Summary. Washington, DC. Retrieved from https://www.eia.gov/consumption/commercial/
Published
2018-09-15
How to Cite
Indriyanti, I., & Subekti, A. (2018). PEMODELAN PREDIKTIF KONSUMSI ENERGI BANGUNAN GEDUNG KOMERSIAL DENGAN ALGORITMA SUPPORT VECTOR MACHINE. Jurnal Pilar Nusa Mandiri, 14(2), 225-232. https://doi.org/10.33480/pilar.v14i2.71
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