Rasyid, Sopian Al (2015) SHORT TERM LOAD FORECASTING UNTUK HARI LIBUR PADA KONDISI BEBAN ANOMALI MENGGUNAKAN ALGORITMA HYBRID BACK PROPAGATION-SWARM PARTICLE. S1 thesis, Universitas Pendidikan Indonesia.
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Abstract
Keakuratan prediksi beban listrik akan berdampak pada biaya pembangkitan yang lebih ekonomis. Penggunaan energi listrik pada hari libur nasional, menunjukkan pola beban yang cenderung tidak identik, pola ini berbeda dari pola beban pada hari normal. Hal tersebut kemudian didefinisikan sebagai beban listrik anomali. Dalam skripsi ini, metode hybrid ANN-Swarm Particel bertujuan untuk memperbaiki akurasi dari prediksi beban listrik anomali yang seringkali terjadi pada hari libur nasional. Metode tersebut digunakan untuk memprediksikan kebutuhan listrik per-setengah jam untuk sistem kelistrikan dalam transmisi listrik nasional di indonesia khsusnya Region Jawa Barat. Penelitian dilakukan dengan cara menguji berbagai nilai learning rate dan input data pembelajaran. Hasil prediksi dari metode ini akan dibandingkan dengan data sesungguhnya yang didapat dari PT.PLN. Hasil penelitian ini menunjukkan bahwa metode tersebut sangatlah efektif untuk memprediksi beban listrik jangka pendek dalam kondisi beban anomali. Hybrid ANN-Swarm Particle cukup sederhana dan mudah sebagai sebuah perangkat analisis para engineer. Kata kunci : Prediksi beban listrik jangka pendek, beban anomali, Jaringan Syaraf Tiruan, Swarm Particle. Load forecast accuracy will have an impact on the generation cost is more economical.The use of electrical energy by consumers on holiday, show the tendency of the load patterns are not identical, it is different from the pattern of the load on a normal day. It is then defined as a anomalous load. In this paper, the method of hybrid ANN-Swarm Particle proposed to improve the accuracy of anomalous load forecasting that often occur on holidays. The proposed methodology has been used to forecast the half-hourly electricity demand for power systems in the Indonesia National Electricity Market in West Java region. Experiments were conducted by testing various of learning rate and learning data input. Performance of this methodology will be validated with real data from the national of electricity company. The result of observations show that the proposed formula is very effective to short-term load forecasting in the case of anomalous load. Hybrid ANN-Swarm Particle relatively simple and easy as a analysis tool by engineers. Keywords : Short term load forecasting, anomalous load, Artificial Neural Network, Swarm Particle.
| Item Type: | Thesis (S1) |
|---|---|
| Additional Information: | No. Panggil: S_TE_RAS s-2015; Pembimbing : I. Ade Gafar Abbullah, II. Yadi Mulyadi |
| Uncontrolled Keywords: | Prediksi beban listrik jangka pendek, beban anomali, Jaringan Syaraf Tiruan, Swarm Particle. |
| Subjects: | L Education > L Education (General) T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General) |
| Divisions: | Fakultas Pendidikan Teknologi dan Kejuruan > Jurusan Pendidikan Teknik Elektro > Program Studi Pendidikan Teknik Elektro |
| Depositing User: | Mr. Cahya Mulyana |
| Date Deposited: | 11 May 2016 07:28 |
| Last Modified: | 11 May 2016 07:28 |
| URI: | http://repository.upi.edu/id/eprint/20034 |
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