Short-Term Forecasting of Electricity Consumption Revenue on Java-Bali Electricity System using Jordan Recurrent Neural Network
Depending on the day and time, electricity consumption tends to fluctuate and directly affects the amount of gained revenue for the company. To anticipate future economic change and to avoid losses in calculating the company’s revenue, it is essential to forecast electricity consumption revenue as a...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Universitas Airlangga
2018-10-01
|
| Series: | Journal of Information Systems Engineering and Business Intelligence |
| Subjects: | |
| Online Access: | https://e-journal.unair.ac.id/JISEBI/article/view/9027 |
| _version_ | 1811159692796231680 |
|---|---|
| author | Tesa Eranti Putri Aji Akbar Firdaus Wilda Imama Sabilla |
| author_facet | Tesa Eranti Putri Aji Akbar Firdaus Wilda Imama Sabilla |
| author_sort | Tesa Eranti Putri |
| collection | DOAJ |
| description | Depending on the day and time, electricity consumption tends to fluctuate and directly affects the amount of gained revenue for the company. To anticipate future economic change and to avoid losses in calculating the company’s revenue, it is essential to forecast electricity consumption revenue as accurate as possible. In this paper, Jordan Recurrent Neural Network (JRNN) was used to do short term forecasting of the electricity consumption revenue from Java-Bali 500 kVA electricity system. Seven JRNN models were trained using electricity consumption revenue between January-March 2012 to predict the revenue of the first week of April 2012. As performance comparators, seven traditional feed forward Artificial Neural Network (ANN) models were also constructed. The forecasting results were as expected for both models, where both producing steady repeating pattern for weekdays, but failed quite poorly to predict the weekends’ revenue. This suggests that in Indonesia, weekends’ electricity consumption revenue has different characteristics than weekdays. Evaluation of the prediction result was carried out using Sum of Square Error (SSE) and Mean Square Error (MSE). The evaluation showed that JRNN produced smaller SSE and MSE values than traditional feed forward ANN, thus JRNN could predict the electricity consumption revenue of Java-Bali electricity system more accurately. |
| first_indexed | 2024-04-10T05:45:29Z |
| format | Article |
| id | doaj.art-8f365f5173444b30beb7dc8c7b8eb896 |
| institution | Directory Open Access Journal |
| issn | 2598-6333 2443-2555 |
| language | English |
| last_indexed | 2024-04-10T05:45:29Z |
| publishDate | 2018-10-01 |
| publisher | Universitas Airlangga |
| record_format | Article |
| series | Journal of Information Systems Engineering and Business Intelligence |
| spelling | doaj.art-8f365f5173444b30beb7dc8c7b8eb8962023-03-06T02:56:32ZengUniversitas AirlanggaJournal of Information Systems Engineering and Business Intelligence2598-63332443-25552018-10-01429610510.20473/jisebi.4.2.96-1057145Short-Term Forecasting of Electricity Consumption Revenue on Java-Bali Electricity System using Jordan Recurrent Neural NetworkTesa Eranti Putri0Aji Akbar Firdaus1Wilda Imama Sabilla2Universitas AirlanggaUniversitas AirlanggaUniversitas AirlanggaDepending on the day and time, electricity consumption tends to fluctuate and directly affects the amount of gained revenue for the company. To anticipate future economic change and to avoid losses in calculating the company’s revenue, it is essential to forecast electricity consumption revenue as accurate as possible. In this paper, Jordan Recurrent Neural Network (JRNN) was used to do short term forecasting of the electricity consumption revenue from Java-Bali 500 kVA electricity system. Seven JRNN models were trained using electricity consumption revenue between January-March 2012 to predict the revenue of the first week of April 2012. As performance comparators, seven traditional feed forward Artificial Neural Network (ANN) models were also constructed. The forecasting results were as expected for both models, where both producing steady repeating pattern for weekdays, but failed quite poorly to predict the weekends’ revenue. This suggests that in Indonesia, weekends’ electricity consumption revenue has different characteristics than weekdays. Evaluation of the prediction result was carried out using Sum of Square Error (SSE) and Mean Square Error (MSE). The evaluation showed that JRNN produced smaller SSE and MSE values than traditional feed forward ANN, thus JRNN could predict the electricity consumption revenue of Java-Bali electricity system more accurately.https://e-journal.unair.ac.id/JISEBI/article/view/9027revenue forecastingelectricity consumptionjava-bali electricity systemjordan recurrent neural network |
| spellingShingle | Tesa Eranti Putri Aji Akbar Firdaus Wilda Imama Sabilla Short-Term Forecasting of Electricity Consumption Revenue on Java-Bali Electricity System using Jordan Recurrent Neural Network Journal of Information Systems Engineering and Business Intelligence revenue forecasting electricity consumption java-bali electricity system jordan recurrent neural network |
| title | Short-Term Forecasting of Electricity Consumption Revenue on Java-Bali Electricity System using Jordan Recurrent Neural Network |
| title_full | Short-Term Forecasting of Electricity Consumption Revenue on Java-Bali Electricity System using Jordan Recurrent Neural Network |
| title_fullStr | Short-Term Forecasting of Electricity Consumption Revenue on Java-Bali Electricity System using Jordan Recurrent Neural Network |
| title_full_unstemmed | Short-Term Forecasting of Electricity Consumption Revenue on Java-Bali Electricity System using Jordan Recurrent Neural Network |
| title_short | Short-Term Forecasting of Electricity Consumption Revenue on Java-Bali Electricity System using Jordan Recurrent Neural Network |
| title_sort | short term forecasting of electricity consumption revenue on java bali electricity system using jordan recurrent neural network |
| topic | revenue forecasting electricity consumption java-bali electricity system jordan recurrent neural network |
| url | https://e-journal.unair.ac.id/JISEBI/article/view/9027 |
| work_keys_str_mv | AT tesaerantiputri shorttermforecastingofelectricityconsumptionrevenueonjavabalielectricitysystemusingjordanrecurrentneuralnetwork AT ajiakbarfirdaus shorttermforecastingofelectricityconsumptionrevenueonjavabalielectricitysystemusingjordanrecurrentneuralnetwork AT wildaimamasabilla shorttermforecastingofelectricityconsumptionrevenueonjavabalielectricitysystemusingjordanrecurrentneuralnetwork |