A Regression-Based Method for Monthly Electric Load Forecasting in South Korea
In this study, we propose a regression-based method for forecasting monthly electricity consumption in South Korea. The regression model incorporates key external variables such as weather conditions, calendar data, and industrial activity to capture the major factors influencing electricity demand....
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/23/5860 |
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| author | Geun-Cheol Lee |
| author_facet | Geun-Cheol Lee |
| author_sort | Geun-Cheol Lee |
| collection | DOAJ |
| description | In this study, we propose a regression-based method for forecasting monthly electricity consumption in South Korea. The regression model incorporates key external variables such as weather conditions, calendar data, and industrial activity to capture the major factors influencing electricity demand. These predictor variables were identified through comprehensive data analysis. Comparative experiments were conducted with various existing methods, including univariate time series models and machine learning techniques like Holt–Winters, LightGBM, and Long Short-Term Memory (LSTM). Additionally, ensemble methods combining two or more of these existing methods were tested. In the empirical analysis, the proposed model was used to forecast monthly electricity demand for a 24-month period (2022–2023), achieving a mean absolute percentage error (MAPE) of approximately 2%. The results demonstrated that the proposed method consistently outperforms all benchmarks tested in this study. |
| format | Article |
| id | doaj-art-2a309f50cc054a22bf9d504c10d9aa5b |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-2a309f50cc054a22bf9d504c10d9aa5b2024-12-13T16:25:10ZengMDPI AGEnergies1996-10732024-11-011723586010.3390/en17235860A Regression-Based Method for Monthly Electric Load Forecasting in South KoreaGeun-Cheol Lee0College of Business Administration, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Republic of KoreaIn this study, we propose a regression-based method for forecasting monthly electricity consumption in South Korea. The regression model incorporates key external variables such as weather conditions, calendar data, and industrial activity to capture the major factors influencing electricity demand. These predictor variables were identified through comprehensive data analysis. Comparative experiments were conducted with various existing methods, including univariate time series models and machine learning techniques like Holt–Winters, LightGBM, and Long Short-Term Memory (LSTM). Additionally, ensemble methods combining two or more of these existing methods were tested. In the empirical analysis, the proposed model was used to forecast monthly electricity demand for a 24-month period (2022–2023), achieving a mean absolute percentage error (MAPE) of approximately 2%. The results demonstrated that the proposed method consistently outperforms all benchmarks tested in this study.https://www.mdpi.com/1996-1073/17/23/5860mid-term load forecastingregressioninteraction effectsmachine learning |
| spellingShingle | Geun-Cheol Lee A Regression-Based Method for Monthly Electric Load Forecasting in South Korea Energies mid-term load forecasting regression interaction effects machine learning |
| title | A Regression-Based Method for Monthly Electric Load Forecasting in South Korea |
| title_full | A Regression-Based Method for Monthly Electric Load Forecasting in South Korea |
| title_fullStr | A Regression-Based Method for Monthly Electric Load Forecasting in South Korea |
| title_full_unstemmed | A Regression-Based Method for Monthly Electric Load Forecasting in South Korea |
| title_short | A Regression-Based Method for Monthly Electric Load Forecasting in South Korea |
| title_sort | regression based method for monthly electric load forecasting in south korea |
| topic | mid-term load forecasting regression interaction effects machine learning |
| url | https://www.mdpi.com/1996-1073/17/23/5860 |
| work_keys_str_mv | AT geuncheollee aregressionbasedmethodformonthlyelectricloadforecastinginsouthkorea AT geuncheollee regressionbasedmethodformonthlyelectricloadforecastinginsouthkorea |