Forecasting of electrical energy consumption using Autoregressive Integrated Moving Average (Case Study: ULP Meulaboh Kota)
Forecasting electricity consumption is one of the solutions that can be implemented by the ULP Meulaboh Kota to ensure the availability of sufficient electricity supply. With the continuous increase in electricity demand, the ULP faces challenges in predicting and managing electricity consumption. U...
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Geuthee Institute
2025-05-01
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| Series: | Journal Geuthee of Engineering and Energy |
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| Online Access: | https://joge.geutheeinstitute.com/index.php/jogee/article/view/56 |
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| author | Putri Gunandra Siregar Ilham Sahputra Fidyatun Nisa |
| author_facet | Putri Gunandra Siregar Ilham Sahputra Fidyatun Nisa |
| author_sort | Putri Gunandra Siregar |
| collection | DOAJ |
| description | Forecasting electricity consumption is one of the solutions that can be implemented by the ULP Meulaboh Kota to ensure the availability of sufficient electricity supply. With the continuous increase in electricity demand, the ULP faces challenges in predicting and managing electricity consumption. Uncertainty in consumption patterns can lead to imbalances between supply and demand, potentially causing various issues such as power outages, high operational costs, and customer dissatisfaction. Therefore, accurate forecasting is essential to support effective decision-making and planning. This study aims to forecast electricity consumption across five different sectors: residential, social, business, industrial, and public, using the ARIMA (Autoregressive Integrated Moving Average) method. The forecasting process involves data collection, stationarity testing using the Augmented Dickey-Fuller (ADF) test, and differencing when necessary to achieve stationarity. The ARIMA model is identified through ACF and PACF plot analysis, estimated, and tested before being used for forecasting. The results indicate that the ARIMA method provides highly accurate forecasts for all sectors, as reflected by the low Mean Absolute Percentage Error (MAPE) values. The residential sector has a MAPE of 4.3957%, the social sector 4.3757%, the business sector 3.1125%, the industrial sector 7.9937%, and the public sector 4.3646%. Overall, the forecasting error produced by the ARIMA model remains below 8%, with an average MAPE of 4.8483% across all sectors. |
| format | Article |
| id | doaj-art-66b9d64ea5c8446da968c158dc6c87e2 |
| institution | Kabale University |
| issn | 2964-2655 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Geuthee Institute |
| record_format | Article |
| series | Journal Geuthee of Engineering and Energy |
| spelling | doaj-art-66b9d64ea5c8446da968c158dc6c87e22025-08-20T03:44:47ZengGeuthee InstituteJournal Geuthee of Engineering and Energy2964-26552025-05-0141243310.52626/joge.v4i1.5632Forecasting of electrical energy consumption using Autoregressive Integrated Moving Average (Case Study: ULP Meulaboh Kota)Putri Gunandra SiregarIlham SahputraFidyatun NisaForecasting electricity consumption is one of the solutions that can be implemented by the ULP Meulaboh Kota to ensure the availability of sufficient electricity supply. With the continuous increase in electricity demand, the ULP faces challenges in predicting and managing electricity consumption. Uncertainty in consumption patterns can lead to imbalances between supply and demand, potentially causing various issues such as power outages, high operational costs, and customer dissatisfaction. Therefore, accurate forecasting is essential to support effective decision-making and planning. This study aims to forecast electricity consumption across five different sectors: residential, social, business, industrial, and public, using the ARIMA (Autoregressive Integrated Moving Average) method. The forecasting process involves data collection, stationarity testing using the Augmented Dickey-Fuller (ADF) test, and differencing when necessary to achieve stationarity. The ARIMA model is identified through ACF and PACF plot analysis, estimated, and tested before being used for forecasting. The results indicate that the ARIMA method provides highly accurate forecasts for all sectors, as reflected by the low Mean Absolute Percentage Error (MAPE) values. The residential sector has a MAPE of 4.3957%, the social sector 4.3757%, the business sector 3.1125%, the industrial sector 7.9937%, and the public sector 4.3646%. Overall, the forecasting error produced by the ARIMA model remains below 8%, with an average MAPE of 4.8483% across all sectors.https://joge.geutheeinstitute.com/index.php/jogee/article/view/56forecastingelectricity consumptionarimaaugmented dickey-fullermape. |
| spellingShingle | Putri Gunandra Siregar Ilham Sahputra Fidyatun Nisa Forecasting of electrical energy consumption using Autoregressive Integrated Moving Average (Case Study: ULP Meulaboh Kota) Journal Geuthee of Engineering and Energy forecasting electricity consumption arima augmented dickey-fuller mape. |
| title | Forecasting of electrical energy consumption using Autoregressive Integrated Moving Average (Case Study: ULP Meulaboh Kota) |
| title_full | Forecasting of electrical energy consumption using Autoregressive Integrated Moving Average (Case Study: ULP Meulaboh Kota) |
| title_fullStr | Forecasting of electrical energy consumption using Autoregressive Integrated Moving Average (Case Study: ULP Meulaboh Kota) |
| title_full_unstemmed | Forecasting of electrical energy consumption using Autoregressive Integrated Moving Average (Case Study: ULP Meulaboh Kota) |
| title_short | Forecasting of electrical energy consumption using Autoregressive Integrated Moving Average (Case Study: ULP Meulaboh Kota) |
| title_sort | forecasting of electrical energy consumption using autoregressive integrated moving average case study ulp meulaboh kota |
| topic | forecasting electricity consumption arima augmented dickey-fuller mape. |
| url | https://joge.geutheeinstitute.com/index.php/jogee/article/view/56 |
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