Muhammad Fiqri Affan, - (2019) PERAMALAN KECEPATAN ANGIN MENGGUNAKAN METODE EXPONENTIAL SMOOTHING DAN ARTIFICIAL NEURAL NETWORK (ANN) UNTUK RENCANA PENGAPLIKASIAN PADA PLTB (PEMBANGKIT LISTRIK TENAGA BAYU) DI KOTA BANDUNG. S1 thesis, Universitas Pendidikan Indonesia.
Abstract
Energi angin merupakan sumber energi yang ramah lingkungan dan efisien yang populer saat ini. Energi angin dapat dikonversikan menjadi energi listrik untuk memenuhi kebutuhan energi listrik di masyarakat. Penelitian ini memaparkan hasil peramalan kecepatan angin kota Bandung selama 5 tahun kedepan yang bertujuan untuk mengetahui potensi kecepatan angin di kota Bandung dan rencana pengaplikasian PLTB (Pembangkit Listrik Tenaga Bayu) demi memenuhi kebutuhan listrik masyarakat, menggunakan metode perhitungan Exponential Smoothing dan Artificial Neural Network (ANN). Proses simulasi perhitungannya menggunakan software Zaitun time-series. Berdasarkan hasil simulasi, nilai peramalan yang paling sedikit nilai errornya didapatkan dengan menggunakan metode Artificial Neural Network (ANN). Hasil penelitian menentukan bahwa Kota Bandung tidak cocok didirikan PLTB karena tidak memenuhi batas minimum kecepatan angin yang cocok untuk sebuah PLTB. Kata kunci : Energi Angin, peramalan, Artificial Neural Network, Exponential Smoothing. ABSTRACT Wind Energy is an environmentally friendly and efficient energy source that is popular today. Wind energy can be converted into electrical energy to meet the electricity needs of the community. This study describes the results of Bandung wind speed forecasting for the next 5 years which are intended to determine the wind speed potential in Bandung and the plan for the application of PLTB (Bayu Power Plant) according to the electricity needs of the community, using the Exponential Smoothing and Artificial Neural Network reporting method (ANN ). The calculation simulation process uses the Zaitun time-series software. Based on the simulation results, the forecasting value with the least error value is obtained using the Artificial Neural Network (ANN) method. The results of the study determined that Bandung City was not suitable for PLTB because it did not meet the minimum limit for speed suitable for PLTB. Keyword : Wind Energy, Forecasting, Artificial Neural Network, Eksponential Smoothing
![[img]](http://repository.upi.edu/style/images/fileicons/text.png) |
Text
S_TE_1504381_Title.pdf Download (4MB) |
|
Text
S_TE_1504381_Chapter1.pdf Download (2MB) |
|
Text
S_TE_1504381_Chapter2.pdf Restricted to Staf Perpustakaan Download (5MB) |
|
Text
S_TE_1504381_Chapter3.pdf Download (2MB) |
|
Text
S_TE_1504381_Chapter4.pdf Restricted to Staf Perpustakaan Download (5MB) |
|
Text
S_TE_1504381_Chapter5.pdf Download (675kB) |
|
Text
S_TE_1504381_Appendix.pdf Restricted to Staf Perpustakaan Download (652kB) |
| Item Type: | Thesis (S1) |
|---|---|
| Additional Information: | No. Panggil: S TE MUH p-2019 ; Pembimbing: I. Ade Gaffar Abdullah, II. Wasimudin Surya ; NIM: 1504381 |
| Uncontrolled Keywords: | Energi Angin, peramalan, Artificial Neural Network, Exponential Smoothing. |
| Subjects: | T Technology > T Technology (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Fakultas Pendidikan Teknik dan Industri > Jurusan Pendidikan Teknik Elektro > Program Studi Teknik Tenaga Elektrik |
| Depositing User: | MUHAMMAD FIQRI AFFAN |
| Date Deposited: | 18 Nov 2019 07:22 |
| Last Modified: | 18 Nov 2019 07:22 |
| URI: | http://repository.upi.edu/id/eprint/37837 |
Actions (login required)
 |
View Item |