PERAMALAN BEBAN LISTRIK HARIAN JAWA TENGAH DAN DIY MENGGUNAKAN METODE SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE
Electrical energy can not be saved in massive scale, because this energy must be provided if the electrical energy which provided more than consumer demand, it means the electrical energy will be wasted. Whereas, if the electrical energy which provided can not fill the consumer demand, there will be...
| Main Authors: | , |
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| Format: | Thesis |
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[Yogyakarta] : Universitas Gadjah Mada
2013
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| _version_ | 1826047605352693760 |
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| author | , SIGIT HARMAWAN , Dr. Ir. Sasongko Pramono Hadi, DEA. |
| author_facet | , SIGIT HARMAWAN , Dr. Ir. Sasongko Pramono Hadi, DEA. |
| author_sort | , SIGIT HARMAWAN |
| collection | UGM |
| description | Electrical energy can not be saved in massive scale, because this energy must be provided if the electrical energy which provided more than consumer demand, it means the electrical energy will be wasted. Whereas, if the electrical energy which provided can not fill the consumer demand, there will be consumer financial loss. The first main condition must be filled up to reach the goal is that electrical company knows the load or electrical demand in the future. Because of the change of electrical power, it needs load forecasts or forecasts of consumer power needs as the basis for operational planning. The goal of this research is to learn if seasonal autoregressive integrated moving average is suitable to daily electrical load forecasting in Central Java and Yogyakarta.
The decision how much data reference used to make forecasts model is one of important thing in electrical load forecasting using seasonal autoregressive integrated moving average (SARIMA) method. The proper selection of sum of data will result minimum forecasting error.
The result of this research shows that the forecast using SARIMA method has high accuracy. It shows that using SARIMA method in electrical load forecasting has higher accuracy than using coefficient method used by PLN. Load forecast using SARIMA result MAPE 1,308 % in work days and 0,668 % in weekend. Whereas, using coefficient method result MAPE 2,16 % in work days and 1,66 % in weekend. The best sum of reference data apply SARIMA method is 192 data. But electrical load forecast using SARIMA method is not suitable for load forecasting in special days such as Idul Fitri and New Year. |
| first_indexed | 2024-03-13T23:13:27Z |
| format | Thesis |
| id | oai:generic.eprints.org:126325 |
| institution | Universiti Gadjah Mada |
| last_indexed | 2024-03-13T23:13:27Z |
| publishDate | 2013 |
| publisher | [Yogyakarta] : Universitas Gadjah Mada |
| record_format | dspace |
| spelling | oai:generic.eprints.org:1263252016-03-04T08:43:14Z https://repository.ugm.ac.id/126325/ PERAMALAN BEBAN LISTRIK HARIAN JAWA TENGAH DAN DIY MENGGUNAKAN METODE SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE , SIGIT HARMAWAN , Dr. Ir. Sasongko Pramono Hadi, DEA. ETD Electrical energy can not be saved in massive scale, because this energy must be provided if the electrical energy which provided more than consumer demand, it means the electrical energy will be wasted. Whereas, if the electrical energy which provided can not fill the consumer demand, there will be consumer financial loss. The first main condition must be filled up to reach the goal is that electrical company knows the load or electrical demand in the future. Because of the change of electrical power, it needs load forecasts or forecasts of consumer power needs as the basis for operational planning. The goal of this research is to learn if seasonal autoregressive integrated moving average is suitable to daily electrical load forecasting in Central Java and Yogyakarta. The decision how much data reference used to make forecasts model is one of important thing in electrical load forecasting using seasonal autoregressive integrated moving average (SARIMA) method. The proper selection of sum of data will result minimum forecasting error. The result of this research shows that the forecast using SARIMA method has high accuracy. It shows that using SARIMA method in electrical load forecasting has higher accuracy than using coefficient method used by PLN. Load forecast using SARIMA result MAPE 1,308 % in work days and 0,668 % in weekend. Whereas, using coefficient method result MAPE 2,16 % in work days and 1,66 % in weekend. The best sum of reference data apply SARIMA method is 192 data. But electrical load forecast using SARIMA method is not suitable for load forecasting in special days such as Idul Fitri and New Year. [Yogyakarta] : Universitas Gadjah Mada 2013 Thesis NonPeerReviewed , SIGIT HARMAWAN and , Dr. Ir. Sasongko Pramono Hadi, DEA. (2013) PERAMALAN BEBAN LISTRIK HARIAN JAWA TENGAH DAN DIY MENGGUNAKAN METODE SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE. UNSPECIFIED thesis, UNSPECIFIED. http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=66545 |
| spellingShingle | ETD , SIGIT HARMAWAN , Dr. Ir. Sasongko Pramono Hadi, DEA. PERAMALAN BEBAN LISTRIK HARIAN JAWA TENGAH DAN DIY MENGGUNAKAN METODE SEASONAL AUTOREGRESSIVE INTEGRATED MOVING AVERAGE |
| title | PERAMALAN BEBAN LISTRIK HARIAN JAWA TENGAH DAN
DIY MENGGUNAKAN METODE SEASONAL
AUTOREGRESSIVE INTEGRATED MOVING AVERAGE |
| title_full | PERAMALAN BEBAN LISTRIK HARIAN JAWA TENGAH DAN
DIY MENGGUNAKAN METODE SEASONAL
AUTOREGRESSIVE INTEGRATED MOVING AVERAGE |
| title_fullStr | PERAMALAN BEBAN LISTRIK HARIAN JAWA TENGAH DAN
DIY MENGGUNAKAN METODE SEASONAL
AUTOREGRESSIVE INTEGRATED MOVING AVERAGE |
| title_full_unstemmed | PERAMALAN BEBAN LISTRIK HARIAN JAWA TENGAH DAN
DIY MENGGUNAKAN METODE SEASONAL
AUTOREGRESSIVE INTEGRATED MOVING AVERAGE |
| title_short | PERAMALAN BEBAN LISTRIK HARIAN JAWA TENGAH DAN
DIY MENGGUNAKAN METODE SEASONAL
AUTOREGRESSIVE INTEGRATED MOVING AVERAGE |
| title_sort | peramalan beban listrik harian jawa tengah dan diy menggunakan metode seasonal autoregressive integrated moving average |
| topic | ETD |
| work_keys_str_mv | AT sigitharmawan peramalanbebanlistrikharianjawatengahdandiymenggunakanmetodeseasonalautoregressiveintegratedmovingaverage AT drirsasongkopramonohadidea peramalanbebanlistrikharianjawatengahdandiymenggunakanmetodeseasonalautoregressiveintegratedmovingaverage |