Electricity consumption forecasting using a novel homogeneous and heterogeneous ensemble learning
In today’s world, a country’s economy is one of the most crucial foundations. However, industries’ financial operations depend on their ability to meet their electricity demands. Thus, forecasting electricity consumption is vital for properly planning and managing energy resources. In this context,...
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| Formato: | Artículo |
| Lenguaje: | inglés |
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2024
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| _version_ | 1881508904417886208 |
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| author | Iftikhar, Hasnain Zywiołek, Justyna López Gonzales, Javier Linkolk Albalawi, Olayan |
| author_browse | Albalawi, Olayan Iftikhar, Hasnain López Gonzales, Javier Linkolk Zywiołek, Justyna |
| author_facet | Iftikhar, Hasnain Zywiołek, Justyna López Gonzales, Javier Linkolk Albalawi, Olayan |
| author_sort | Iftikhar, Hasnain |
| collection | Producción científica |
| description | In today’s world, a country’s economy is one of the most crucial foundations. However, industries’ financial operations depend on their ability to meet their electricity demands. Thus, forecasting electricity consumption is vital for properly planning and managing energy resources. In this context, a new approach based on ensemble learning has been developed to predict monthly electricity consumption. The method divides electricity consumption time series into deterministic and stochastic components. The deterministic component, which consists of a secular long-term trend and an annual seasonality, is estimated using a multiple regression model. In contrast, the stochastic part considers the short-run random fluctuations of the consumption time series. It is forecasted by four different time series, four machine learning models, and three novel proposed ensemble models: the time series homogeneous ensemble model, the machine learning ensemble model, and the heterogeneous ensemble model. The study analyzed data on Pakistan’s monthly electricity consumption from 1991-January to 2022-December. The evaluation of the forecasting models is based on three criteria: accuracy metrics (including the mean absolute percent error (MAPE), the mean absolute error (MAE), the root mean squared error (RMSE), and the root relative squared error (RRSE)); an equality forecast statistical test (the Diebold and Mariano’s test); and a graphical assessment. The heterogeneous ensemble model’s forecasting results show lower error values compared to the homogeneous ensemble models and the singles models, with accuracy metrics measured by MAPE, MAE, RMSE, and RRSE at 5.0027, 460.4800, 614.5276, and 0.2933, respectively. Additionally, the heterogeneous ensemble model is statistically significant (p < 0.05) and superior to the rest of the models. Also, the heterogeneous ensemble model demonstrates considerable performance with the least mean error, which is comparatively better than the individual and best models reported in the literature and are considered baseline models. Further, the forecast values’ monthly behavior depicts that electricity consumption is higher during the summer season, and this demand will be highest in June and July. The forecast model and graph reveal that electricity consumption rapidly increases with time. This indirectly indicates that the government of Pakistan must take adequate steps to improve electricity production through different energy sources to restore the country’s economic status by meeting the country’s electricity demand. Despite several studies conducted from various perspectives, no analysis has been undertaken using an ensemble learning approach to forecast monthly electricity consumption for Pakistan. |
| format | Article |
| id | oai:cris.upeu.edu.pe:20.500.15037/412 |
| institution | UPeU |
| language | English |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| record_format | dspace |
| spelling | oai:cris.upeu.edu.pe:20.500.15037/4122026-10-30T16:14:36ZElectricity consumption forecasting using a novel homogeneous and heterogeneous ensemble learningIftikhar, HasnainZywiołek, JustynaLópez Gonzales, Javier LinkolkAlbalawi, OlayanPakistan electricity consumptionmonthly electricity consumption forecastingtimes series modelsmachine learning modelshomogeneous and heterogeneous ensemble learning modelsIn today’s world, a country’s economy is one of the most crucial foundations. However, industries’ financial operations depend on their ability to meet their electricity demands. Thus, forecasting electricity consumption is vital for properly planning and managing energy resources. In this context, a new approach based on ensemble learning has been developed to predict monthly electricity consumption. The method divides electricity consumption time series into deterministic and stochastic components. The deterministic component, which consists of a secular long-term trend and an annual seasonality, is estimated using a multiple regression model. In contrast, the stochastic part considers the short-run random fluctuations of the consumption time series. It is forecasted by four different time series, four machine learning models, and three novel proposed ensemble models: the time series homogeneous ensemble model, the machine learning ensemble model, and the heterogeneous ensemble model. The study analyzed data on Pakistan’s monthly electricity consumption from 1991-January to 2022-December. The evaluation of the forecasting models is based on three criteria: accuracy metrics (including the mean absolute percent error (MAPE), the mean absolute error (MAE), the root mean squared error (RMSE), and the root relative squared error (RRSE)); an equality forecast statistical test (the Diebold and Mariano’s test); and a graphical assessment. The heterogeneous ensemble model’s forecasting results show lower error values compared to the homogeneous ensemble models and the singles models, with accuracy metrics measured by MAPE, MAE, RMSE, and RRSE at 5.0027, 460.4800, 614.5276, and 0.2933, respectively. Additionally, the heterogeneous ensemble model is statistically significant (p < 0.05) and superior to the rest of the models. Also, the heterogeneous ensemble model demonstrates considerable performance with the least mean error, which is comparatively better than the individual and best models reported in the literature and are considered baseline models. Further, the forecast values’ monthly behavior depicts that electricity consumption is higher during the summer season, and this demand will be highest in June and July. The forecast model and graph reveal that electricity consumption rapidly increases with time. This indirectly indicates that the government of Pakistan must take adequate steps to improve electricity production through different energy sources to restore the country’s economic status by meeting the country’s electricity demand. Despite several studies conducted from various perspectives, no analysis has been undertaken using an ensemble learning approach to forecast monthly electricity consumption for Pakistan.Articlehttps://doi.org/10.3389/fenrg.2024.144250210.3389/fenrg.2024.1442502https://hdl.handle.net/20.500.15037/412W4402222380enFrontiers in Energy Research2296-598X2024-09-042026-06-13T08:13:00Z2026-06-13T08:13:00Z |
| spellingShingle | Pakistan electricity consumption monthly electricity consumption forecasting times series models machine learning models homogeneous and heterogeneous ensemble learning models Iftikhar, Hasnain Zywiołek, Justyna López Gonzales, Javier Linkolk Albalawi, Olayan Electricity consumption forecasting using a novel homogeneous and heterogeneous ensemble learning |
| title | Electricity consumption forecasting using a novel homogeneous and heterogeneous ensemble learning |
| title_full | Electricity consumption forecasting using a novel homogeneous and heterogeneous ensemble learning |
| title_fullStr | Electricity consumption forecasting using a novel homogeneous and heterogeneous ensemble learning |
| title_full_unstemmed | Electricity consumption forecasting using a novel homogeneous and heterogeneous ensemble learning |
| title_short | Electricity consumption forecasting using a novel homogeneous and heterogeneous ensemble learning |
| title_sort | electricity consumption forecasting using a novel homogeneous and heterogeneous ensemble learning |
| topic | Pakistan electricity consumption monthly electricity consumption forecasting times series models machine learning models homogeneous and heterogeneous ensemble learning models |
| topic_facet | Pakistan electricity consumption monthly electricity consumption forecasting times series models machine learning models homogeneous and heterogeneous ensemble learning models |
| url | https://doi.org/10.3389/fenrg.2024.1442502 https://hdl.handle.net/20.500.15037/412 |
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