Prediksi Kecepatan Angin untuk Mengetahui Potensi Sumber Energi Alternatif menggunakan Model Regresi Lasso: Studi Kasus Kota Makassar pada Tahun 2024
DOI:
https://doi.org/10.55606/juprit.v3i1.3501Keywords:
Wind Speed Prediction, Lasso Regression Model, Alternative Energy Source, MakassarAbstract
This research explores the potential of wind energy as an alternative energy source in Makassar City. The researcher used daily climate data from BMKG Martim Paotere Meteorological Station Makassar City for 2023 to January 2024. The research method uses the Lasso regression model to predict wind speed. The results of data processing, through tests with an MSE value of 0.334 and an R2 value of 0.97, show the high validity of the model. Wind speed predictions for 2024 were then generated and converted into estimates of the electrical power that could be generated. Based on this prediction, the maximum wind speed reached 10.76 m/s, with the maximum electrical power reaching 1597 Watts. The results of this study indicate that Makassar City has considerable potential to be developed as a Wind Power Plant location as an alternative source of electrical energy. This potential can contribute to reducing dependence on conventional energy in Makassar City.
References
BMKG. (2023). Pusat Database BMKG - Data Online. https://dataonline.bmkg.go.id/data_iklim
Darmawan, N. R. (2019). Prediksi Kondisi Cuaca Kota Surabaya Menggunakan Metode Artificial Neural Network [Institut Teknologi Sepuluh Nopember]. https://repository.its.ac.id/60532/1/05211440000193-Undergraduate_Theses.pdf
Fitria, E. R., & Rozci, F. (2023). Penerapan Metode Regresi Least Absolute Shrinkage and Selection Operator (LASSO) dan Regresi Linier untuk Memprediksi Tingkat Kemiskinan di Indonesia. Jurnal Ilmiah Sosio Agribis, 22(2), 123. https://doi.org/10.30742/jisa22220222620
Hadi, S. I., Ermatita, & Chamidah, N. (2022). Penerapan Fuzzy C-Means dan Fuzzy Sugeno dalam Memprediksi Cuaca. 4221(April), 11–22.
Karaman, Ö. A. (2023). Prediction of Wind Power with Machine Learning Models. Applied Sciences, 13(20), 11455. https://doi.org/10.3390/app132011455
Putri, I. P., Terttiaavini, T., & Arminarahmah, N. (2024). Analisis Perbandingan Algoritma Machine Learning untuk Prediksi Stunting pada Anak. Indonesian Journal of Machine Learning and Computer Science, 4. https://doi.org/https://doi.org/10.57152/malcom.v4i1.1078
Rawal, A. R., Hamzah, B., & Mulyadi, R. (2023). Analisis potensi angin dan penggunaan turbin angin pada bangunan tinggi yang terletak di sisi barat Kota Makassar (Studi kasus: Delft Apartemen). 17, 252–261. https://doi.org/https://doi.org/10.24252/teknosains.v17i2.37677
Reza Rifaldi, D. A. R. (2023, November). Pemadaman Listrik Bergilir di Makassar Capai 5-6 Jam, PLN Beri Penjelasan. Kompas.Com. https://makassar.kompas.com/read/2023/11/27/172431478/pemadaman-listrik-bergilir-di-makassar-capai-5-6-jam-pln-beri-penjelasan
Sachi, H., Kevin, I., & Yonathan, A. (2023). Klasifikasi Jenis Kelamin Berdasarkan Citra Wajah menggunakan Metode Deep Learning. The Journal on Machine Learning and Computational Intelligence (JMLCI), 9–17.
Wardhana, R. G., Wang, G., & Sibuea, F. (2023). Penerapan Machine Learning Dalam Prediksi Tingkat Kasus Penyakit Di Indonesia. Journal of Information System Management (JOISM), 5(1), 40–45. https://doi.org/10.24076/joism.2023v5i1.1136
Zhang, W., Wang, D., Sun, Z., Song, J., & Deng, X. (2021). Robust superhydrophobicity: mechanisms and strategies. Chem. Soc. Rev., 50(6), 4031–4061. https://doi.org/10.1039/D0CS00751J
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