Prediksi Temperatur Lingkungan dengan Recurrent Neural Network Menggunakan Data Historis Iradiasi Matahari
I will put the dimension here
Abstract
Penelitian ini mempelajari penggunaan Recurrent Neural Network (RNN) untuk memprediksi temperatur lingkungan di kota London menggunakan data historis iradiasi matahari. Data yang digunakan terdiri dari Hi temperature, low temperature, temperature out, dan iradiasi matahari yang dikumpulkan selama 24 jam dari bulan maret 2014 untuk memprediksi bulan april 2014, dengan 80% untuk pelatihan, 10% untuk validasi, dan 10% untuk testing. Hasil penelitian menunjukkan bahwa RNN dapat melakukan prediksi dengan baik dan memberikan hasil yang stabil dan konsisten. RMSE yang didapat untuk prediksi hi temperature, low temperature, temperature out, dan iradiasi matahari adalah 4.97, 4.20, 4.48, dan 5.03 Penelitian ini diharapkan dapat membantu dalam memprediksi kondisi temperatur pada lingkungan. Hasil penelitian menunjukkan bahwa RNN dapat memprediksi temperatur lingkungan dengan akurasi yang tinggi.
References
[1] Y. Makasudede, “RNN dengan tipe LSTM,” pp. 8–45, 2019.
[2] Y. E. N. Nugraha, I. Ariawan, and W. A. Arifin, “Weather Forecast From Time Series Data Using Lstm Algorithm,” J. Teknol. Inf. Dan Komun., vol. 14, no. 1, pp. 144–152, 2023, doi: 10.51903/jtikp.v14i1.531.
[3] E. A. Nketiah, L. Chenlong, J. Yingchuan, and S. A. Aram, “Recurrent neural network modeling of multivariate time series and its application in temperature forecasting,” PLoS One, vol. 18, no. 5 May, pp. 1–18, 2023, doi: 10.1371/journal.pone.0285713.
[4] J. M. Han, Y. Q. Ang, A. Malkawi, and H. W. Samuelson, “Using recurrent neural networks for localized weather prediction with combined use of public airport data and on-site measurements,” Build. Environ., vol. 192, p. 107601, 2021, doi: 10.1016/j.buildenv.2021.107601.
[5] P. R. Shekar, A. Mathew, P. V. Yeswanth, and S. Deivalakshmi, “A combined deep CNN-RNN network for rainfall-runoff modelling in Bardha Watershed, India,” Artif. Intell. Geosci., vol. 5, no. July 2023, p. 100073, 2024, doi: 10.1016/j.aiig.2024.100073.
[6] I. Sri Rahayu, E. C. Djamal, R. Ilyas, and A. T. Bon, “Daily temperature prediction using recurrent neural networks and long-short term memory,” Proc. Int. Conf. Ind. Eng. Oper. Manag., no. August, pp. 2700–2709, 2020.
[7] N. T. Luchia, E. Tasia, I. Ramadhani, A. Rahmadeyan, and R. Zahra, “Performance Comparison Between Artificial Neural Network, Recurrent Neural Network and Long Short-Term Memory for Prediction of Extreme Climate Change,” Public Res. J. Eng. Data Technol. Comput. Sci., vol. 1, no. 2, pp. 62–70, 2024, doi: 10.57152/predatecs.v1i2.864.
[8] Y. E. Cebeci, “A Recurrent Neural Network Model for Weather Forecasting,” UBMK 2019 - Proceedings, 4th Int. Conf. Comput. Sci. Eng., pp. 591–595, 2019, doi: 10.1109/UBMK.2019.8907196.
[9] B. Zhao, X. Li, X. Lu, and Z. Wang, “A CNN–RNN architecture for multi-label weather recognition,” Neurocomputing, vol. 322, pp. 47–57, 2018, doi: 10.1016/j.neucom.2018.09.048.
[10] London Data Store. Photovoltaic (PV) Solar Panel Energy Generation data [Online] (update 2 years ago)Tersedia: https://data.london.gov.uk/dataset/photovoltaic--pv--solar-panel-energy-generation-data
Copyright (c) 2024 Raihan Aldiaz Rahman, Pola Risma, Yurni Oktarina, Hendra Marta Yudha
This work is licensed under a Creative Commons Attribution 4.0 International License.
