Prediksi Kecepatan Angin Jangka Menengah dengan Artificial Neural Network untuk Estimasi Daya Listrik Tenaga Angin (Studi Kasus: Kota Sabang)
Indonesia, as a country at the equator, has a very large renewable energy potential that can be used as a source of electrical energy. Electricity consumption in Indonesia, especially in Aceh, continues to increase annually because of population growth and increasing economic needs. Recently, the co...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
University of Serambi Mekkah
2023-05-01
|
| Series: | Jurnal Serambi Engineering |
| Subjects: | |
| Online Access: | https://ojs.serambimekkah.ac.id/jse/article/view/6010 |
| _version_ | 1797755246088290304 |
|---|---|
| author | Abdul Malek Suriadi Suriadi Khairun Saddami |
| author_facet | Abdul Malek Suriadi Suriadi Khairun Saddami |
| author_sort | Abdul Malek |
| collection | DOAJ |
| description | Indonesia, as a country at the equator, has a very large renewable energy potential that can be used as a source of electrical energy. Electricity consumption in Indonesia, especially in Aceh, continues to increase annually because of population growth and increasing economic needs. Recently, the construction of power plants has been considered to be environmentally friendly and economical. One of the efforts that can be made is the development of wind-power plants. The availability of certain wind speeds was expected. Therefore, accurate prediction data must be used as the basis for building wind power plants. To increase the accuracy of wind speed prediction by looking at the error rate in predicting the amount of wind speed generated using an Artificial Neural Network with feed-forward and feed-backward functions from the back propagation algorithm (BPNN). The results of the application using the Neural Network algorithm with a back propagation Neural Network (BPNN) to predict wind speed show that the Neural Network algorithm can predict wind speed with an error of 0.0036. In July 2021, the estimated energy demand is 81.5 KWH. |
| first_indexed | 2024-03-12T17:45:04Z |
| format | Article |
| id | doaj.art-d53451a5b4a442a39bbc21886908ab2b |
| institution | Directory Open Access Journal |
| issn | 2528-3561 2541-1934 |
| language | English |
| last_indexed | 2024-03-12T17:45:04Z |
| publishDate | 2023-05-01 |
| publisher | University of Serambi Mekkah |
| record_format | Article |
| series | Jurnal Serambi Engineering |
| spelling | doaj.art-d53451a5b4a442a39bbc21886908ab2b2023-08-03T19:13:01ZengUniversity of Serambi MekkahJurnal Serambi Engineering2528-35612541-19342023-05-018310.32672/jse.v8i3.60104472Prediksi Kecepatan Angin Jangka Menengah dengan Artificial Neural Network untuk Estimasi Daya Listrik Tenaga Angin (Studi Kasus: Kota Sabang)Abdul Malek0Suriadi Suriadi1Khairun Saddami2Teknik Elektro, Fakultas Teknik, Universitas Syiah Kuala Banda Aceh IndonesiaTeknik Elektro, Fakultas Teknik, Universitas Syiah Kuala Banda Aceh IndonesiaTeknik Elektro, Fakultas Teknik, Universitas Syiah Kuala Banda Aceh IndonesiaIndonesia, as a country at the equator, has a very large renewable energy potential that can be used as a source of electrical energy. Electricity consumption in Indonesia, especially in Aceh, continues to increase annually because of population growth and increasing economic needs. Recently, the construction of power plants has been considered to be environmentally friendly and economical. One of the efforts that can be made is the development of wind-power plants. The availability of certain wind speeds was expected. Therefore, accurate prediction data must be used as the basis for building wind power plants. To increase the accuracy of wind speed prediction by looking at the error rate in predicting the amount of wind speed generated using an Artificial Neural Network with feed-forward and feed-backward functions from the back propagation algorithm (BPNN). The results of the application using the Neural Network algorithm with a back propagation Neural Network (BPNN) to predict wind speed show that the Neural Network algorithm can predict wind speed with an error of 0.0036. In July 2021, the estimated energy demand is 81.5 KWH.https://ojs.serambimekkah.ac.id/jse/article/view/6010wind speed, artificial neural network, electrical energy, sabang, wind power |
| spellingShingle | Abdul Malek Suriadi Suriadi Khairun Saddami Prediksi Kecepatan Angin Jangka Menengah dengan Artificial Neural Network untuk Estimasi Daya Listrik Tenaga Angin (Studi Kasus: Kota Sabang) Jurnal Serambi Engineering wind speed, artificial neural network, electrical energy, sabang, wind power |
| title | Prediksi Kecepatan Angin Jangka Menengah dengan Artificial Neural Network untuk Estimasi Daya Listrik Tenaga Angin (Studi Kasus: Kota Sabang) |
| title_full | Prediksi Kecepatan Angin Jangka Menengah dengan Artificial Neural Network untuk Estimasi Daya Listrik Tenaga Angin (Studi Kasus: Kota Sabang) |
| title_fullStr | Prediksi Kecepatan Angin Jangka Menengah dengan Artificial Neural Network untuk Estimasi Daya Listrik Tenaga Angin (Studi Kasus: Kota Sabang) |
| title_full_unstemmed | Prediksi Kecepatan Angin Jangka Menengah dengan Artificial Neural Network untuk Estimasi Daya Listrik Tenaga Angin (Studi Kasus: Kota Sabang) |
| title_short | Prediksi Kecepatan Angin Jangka Menengah dengan Artificial Neural Network untuk Estimasi Daya Listrik Tenaga Angin (Studi Kasus: Kota Sabang) |
| title_sort | prediksi kecepatan angin jangka menengah dengan artificial neural network untuk estimasi daya listrik tenaga angin studi kasus kota sabang |
| topic | wind speed, artificial neural network, electrical energy, sabang, wind power |
| url | https://ojs.serambimekkah.ac.id/jse/article/view/6010 |
| work_keys_str_mv | AT abdulmalek prediksikecepatananginjangkamenengahdenganartificialneuralnetworkuntukestimasidayalistriktenagaanginstudikasuskotasabang AT suriadisuriadi prediksikecepatananginjangkamenengahdenganartificialneuralnetworkuntukestimasidayalistriktenagaanginstudikasuskotasabang AT khairunsaddami prediksikecepatananginjangkamenengahdenganartificialneuralnetworkuntukestimasidayalistriktenagaanginstudikasuskotasabang |