A case study on modelling and forecasting electricity demand data using double seasonal arima
The substantial electricity demand data necessitates modelling and forecasting using an extension of the Seasonal Autoregressive Integrated Moving Average (SARIMA), known as Double SARIMA (DSARIMA). DSARIMA is suitable since the electricity data exhibits two seasonalities. However, researchers often...
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| Format: | Article |
| Language: | English |
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Pushpa Publishing House
2025
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| Online Access: | https://umpir.ump.edu.my/id/eprint/45337/ |
| _version_ | 1848827387948564480 |
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| author | Puteri Aiman Syahirah, Rosman Nur Haizum, Abd Rahman |
| author_facet | Puteri Aiman Syahirah, Rosman Nur Haizum, Abd Rahman |
| author_sort | Puteri Aiman Syahirah, Rosman |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | The substantial electricity demand data necessitates modelling and forecasting using an extension of the Seasonal Autoregressive Integrated Moving Average (SARIMA), known as Double SARIMA (DSARIMA). DSARIMA is suitable since the electricity data exhibits two seasonalities. However, researchers often employed the multiplicative model to analyze seasonal data without considering the alternative models, additive and subset. Thus, this study incorporates all DSARIMA, additive, multiplicative, and subset models in forecasting electricity demand data while comparing different parameter estimation methods: Maximum Likelihood (ML) and least squares. The dataset from the United Kingdom for the years 2022 and 2023 was utilized in this study. The analysis accounted for seasonal patterns with half-hourly and weekly intervals, represented by 48 and 336 periods, respectively. The results reveal that the subset DSARIMA model with the least square estimation method produces the highest forecasting accuracy with the lowest Mean Absolute Percentage Error (MAPE) value compared to the other possible models for all forecasting horizons, ranging from one to four weeks. This study has demonstrated the significance of considering different DSARIMA models with alternative parameter estimation methods. This, ultimately, ensures more accurate and reliable predictions in double seasonal data. |
| first_indexed | 2025-11-15T03:59:55Z |
| format | Article |
| id | ump-45337 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:59:55Z |
| publishDate | 2025 |
| publisher | Pushpa Publishing House |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-453372025-08-11T06:51:30Z https://umpir.ump.edu.my/id/eprint/45337/ A case study on modelling and forecasting electricity demand data using double seasonal arima Puteri Aiman Syahirah, Rosman Nur Haizum, Abd Rahman HA Statistics QA Mathematics The substantial electricity demand data necessitates modelling and forecasting using an extension of the Seasonal Autoregressive Integrated Moving Average (SARIMA), known as Double SARIMA (DSARIMA). DSARIMA is suitable since the electricity data exhibits two seasonalities. However, researchers often employed the multiplicative model to analyze seasonal data without considering the alternative models, additive and subset. Thus, this study incorporates all DSARIMA, additive, multiplicative, and subset models in forecasting electricity demand data while comparing different parameter estimation methods: Maximum Likelihood (ML) and least squares. The dataset from the United Kingdom for the years 2022 and 2023 was utilized in this study. The analysis accounted for seasonal patterns with half-hourly and weekly intervals, represented by 48 and 336 periods, respectively. The results reveal that the subset DSARIMA model with the least square estimation method produces the highest forecasting accuracy with the lowest Mean Absolute Percentage Error (MAPE) value compared to the other possible models for all forecasting horizons, ranging from one to four weeks. This study has demonstrated the significance of considering different DSARIMA models with alternative parameter estimation methods. This, ultimately, ensures more accurate and reliable predictions in double seasonal data. Pushpa Publishing House 2025-08 Article PeerReviewed pdf en cc_by_4 https://umpir.ump.edu.my/id/eprint/45337/1/article/view/3009 Puteri Aiman Syahirah, Rosman and Nur Haizum, Abd Rahman (2025) A case study on modelling and forecasting electricity demand data using double seasonal arima. Advances and Applications in Statistics, 92 (9). pp. 1271-1286. ISSN 0972-3617. (Published) https://doi.org/10.17654/0972361725057 https://doi.org/10.17654/0972361725057 https://doi.org/10.17654/0972361725057 |
| spellingShingle | HA Statistics QA Mathematics Puteri Aiman Syahirah, Rosman Nur Haizum, Abd Rahman A case study on modelling and forecasting electricity demand data using double seasonal arima |
| title | A case study on modelling and forecasting electricity demand data using double seasonal arima |
| title_full | A case study on modelling and forecasting electricity demand data using double seasonal arima |
| title_fullStr | A case study on modelling and forecasting electricity demand data using double seasonal arima |
| title_full_unstemmed | A case study on modelling and forecasting electricity demand data using double seasonal arima |
| title_short | A case study on modelling and forecasting electricity demand data using double seasonal arima |
| title_sort | case study on modelling and forecasting electricity demand data using double seasonal arima |
| topic | HA Statistics QA Mathematics |
| url | https://umpir.ump.edu.my/id/eprint/45337/ https://umpir.ump.edu.my/id/eprint/45337/ https://umpir.ump.edu.my/id/eprint/45337/ |