Analisis prakiraan kecepatan angin dengan menggunakan artificial neural network

Afrah Halawani, Sutjianto S, Mardiana Irawaty

Abstract


Prediction is one of the most important techniques in determining the resulting wind speed. The decision to predict is very important, because with predictions can calculate the average wind speed and a good prediction is an accurate prediction. The purpose of this research is to predict the wind speed using the neural network model to determine the wind speed in the following year. To predict wind speed, the author builds a wind speed prediction model using an artificial neural network (ANN) with a backpropagation learning algorithm. The scope of research data collection from BMKG Sleman. Therefore we need a method that is better than the load coefficient method. The results of this study show that the predictions of ANN wind speed in 2019 from January to December are close to the default data from the BMKG. Sleman and stay stable. And the average error percentage of ANN is 5%.

 



Keywords


Wind speed, prediction, artificial neural network, backpropagation

References


Akhmad Fadholi.(2013). “Pemanfaatan Suhu Udara dan Kelembapan Udara Dalam Persamaan Regresi Untuk Simulasi Prediksi Total Hujan Bulanan Di Pangkal Pinang”. Pangkal Pinang.

Akhmad Fadholi. (2013), “Analisis data Arah dan Kecepatan Angin Landas Pacu.

(Runway) Menggunakan aplikasi Windrose Plot (WRPLOT”), Pangkal Pinang.

Anggit Bimo.(2020). “Analisis Prakiraan Beban Listrik Wilayah Yogyakarta dengan Jaringan Syaraf Tiruan” Yogyakarta

Desvina, A. P., & Anggriani, M.(2015). “Peramalan Kecepatan Angin Di Kota Pekanbaru Menggunakan Metode Box-Jenkins. Jurnal Sains Matematika Dan Statistika”. Pekanbaru.

Ema Sastri Puspita, Liza Yulianti. (2016). “Perancangan Sistem Peramalan Cuaca Berbasis Logika Fuzzy”. Bengkulu.

Gunawan D, Sudarsono, Wahyuono S, Donatus IA, Purnomo. (2011). “Cuaca: Hasil Penelitian, Sifat-sifat dan Penggunaan”.

https://socs.binus.ac.id/2012/07/26/konsep-neural-network

diakses pada 15 April 2020

H. S. Nogay. (2012), “Application of Artificial Neural Network s for short term wind speed forecasting in Mardin , Turkey”. Afrika Selatan

Saputra Rizqilillah. (2018). “Rancang Bangun Alat Penentu Arah Landing Pesawat Menggunakan Anemometer JL FS2” Yogyakarta.

Syukri , Samsuddin. (2018) “Pengujian Algoritma Artificial Neural Network

(ANN) Untuk Prediksi Kecepatan Angin”. Universitas Serambi Mekkah.

Syukur Abdul. (2016). “Prediksi Kecepatan Angin Menggunakan Model Artificial Neural Network Berbasis Adaboost” Semarang.

https://hwsmartsolution.com/blog/2016/02/18/metode-lvq-learning-vector-quantization-untuk-pengenalan-pola/

diakses pada 20 April 2020




DOI: http://dx.doi.org/10.28989/senatik.v6i0.424

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Conference SENATIK P-ISSN :2337-3881 and  E-ISSN : 2528-1666

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Statistik Senatik

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