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Journal : Journal of Information Systems and Technology Research

Comparative Analysis of Deep Learning Models for Wind Speed Prediction Using LSTM, TCN and RBFNN Wardani, Firly Setya; Idhom, Mohammad; Aviolla Terza Damaliana
Journal of Information Systems and Technology Research Vol. 4 No. 3 (2025): September 2025
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/jistr.v4i3.1298

Abstract

Wind speed forecasting plays a vital role in various sectors, including renewable energy management and disaster preparedness for extreme weather events. Accurate prediction models are essential to support decision-making processes, especially in regions with dynamic seasonal patterns. This study compares the performance of three time series prediction models Long Short-Term Memory (LSTM), Temporal Convolutional Network (TCN), and Radial Basis Function Neural Network (RBFNN) for forecasting daily wind speed. The dataset consists of historical wind speed data that underwent multiple preprocessing steps, including seasonal-based missing value imputation, stationarity testing, supervised transformation, normalization, and hyperparameter tuning to optimize model performance. The models were evaluated using four standard regression metrics: Mean Absolute Error (MAE), Root Mean Square Error (RMSE), R-Squared (R²), and Mean Absolute Percentage Error (MAPE). The results show that the TCN model outperformed the others, achieving an MAE of 1.117, RMSE of 1.524, R² of 0.120, and MAPE of 20.95%. The LSTM model ranked second with competitive performance, while the RBFNN model produced consistent but slightly lower accuracy. The findings highlight the superiority of TCN in capturing complex sequential and seasonal patterns in wind speed data. The unique contribution of this research lies in integrating seasonal-based preprocessing with a comparative evaluation of three advanced models under varying conditions, including extreme weather scenarios. This study serves as a foundation for developing more accurate and reliable wind speed forecasting systems to support renewable energy planning and enhance disaster risk mitigation strategies.
Co-Authors Adam, Cindi Akbar , Fawwaz Ali Alfan Rizaldy Pratama Alif, Rahmat Istighfaroni Aminullah, Ahmad Adiib Angga, Angga Rahmad Purnama Anggraini Puspita Sari Anniswa, Iqbal Ramadhan Aviolla Terza Damaliana Azzahra, Adelia Ramadhina Bajramaya, Dewa Widya Basuki Rahmat Masdi Siduppa Cahaya Purtri Agustika Carissa, Savvy Prissy Amellia Damaliana, Aviolla Terza Dewi , Deshinta Arrova Diash, Hakam Dzakwan Diyasa, I Gede Susrama Mas Dwi Arman Prasetya Fahrudin, Tresna Maulana Gede Susrama Mas Diyasa, I Gunawan, Boy Erdyansyah Halim, Rahman Nur Harahap, Jasmine Avrile Kaniasari Henni Endah Wahanani Jauharis Saputra, Wahyu Syaifullah JS, Wahyu Syaifullah Kartika Maulida Hindrayani Khasanah, Ema Isfa'atin Kristiawan, Kiki Yuniar Kurniawati, Dyah Ayu Listyo Kuswardhani , Hajjar Ayu Cahyani Lidya Musaffak, Awal Linggasari, Dienna Eries Lisanthoni, Angela Maulana, Hendra Maulida Hindrayani, Kartika Maulida, Kartika Muhaimin, Amri Muhammad Rizki Alamsyah Muhammad Thoriqulhaq Mutiara Irmadhani Nabila, Nasywa Azzah Nafiah, Fajria Ulumin Nariyana, Calvien Danny Nathania, Vannesa naufal firdaus, ahmad Pamungkas, Syahrul Ardi Panglima, Talitha Fujisai Permadani, Citra Amelia Intan Priananda, Arya Mahardika Putri, Deannisa Syafira Putri, Deva Amalia Rahma Ramadani, Nurmalita Ramadhan Anniswa, Iqbal Raynaldi, Achmad Riyantoko, Prismahardi Aji Ryan Dana, Alvin Saputra, Wahyu Syaifullah Jauharis Shaffa Ameera, Divanda Sugiarti, Nova Putri Dwi Susrama Mas Diyasa, I Gede Syaifullah J. S, Wahyu Syaifullah JS, Wahyu Terza Damaliana, Aviolla Thohir, A. Zaki Thoriqulhaq, Muhammad Trimono Trimono, Trimono Wardana, Azel Christian Wardani, Firly Setya Widi Saputro, Tegar Windyadari, Chrysilla Citra