A new grey seasonal multivariate forecasting model and its application in energy consumption
Accurate energy consumption forecasting is of critical importance, as it enables governments, industries, and individuals to effectively plan for energy supply and demand, thereby reducing the risks associated with both oversupply and shortages. While prior research has explored various forecasting...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-05-01
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| Series: | Energy Strategy Reviews |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2211467X25001208 |
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| author | Jianming Jiang Yandong Ban Ming Zhang Chiwen Qu |
| author_facet | Jianming Jiang Yandong Ban Ming Zhang Chiwen Qu |
| author_sort | Jianming Jiang |
| collection | DOAJ |
| description | Accurate energy consumption forecasting is of critical importance, as it enables governments, industries, and individuals to effectively plan for energy supply and demand, thereby reducing the risks associated with both oversupply and shortages. While prior research has explored various forecasting models, relatively few have specifically addressed the challenges of seasonal forecasting under multivariate conditions. To bridge this gap, this paper proposes a novel model—the Discrete Nonlinear Grey Bernoulli Model (DNGBM). At the heart of DNGBM is a set of dynamic parameters specifically designed to capture seasonal variation, making the model well-suited for periodic energy consumption patterns. The model also introduces nonlinear parameters that broaden its representational capacity, thereby enhancing modeling flexibility and accuracy. This dual enhancement enables DNGBM to outperform traditional grey models in both predictive precision and adaptability across diverse energy forecasting scenarios. In the two provided cases, the new model's average results for MAPE, MAE, MSE, and R2 are 3.664 %, 65.596, 11182.902, and 0.952, respectively, outperforming other competing models, which validates its effectiveness. |
| format | Article |
| id | doaj-art-e35fdfd6fa5b44ae863c72648a960a09 |
| institution | Kabale University |
| issn | 2211-467X |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Strategy Reviews |
| spelling | doaj-art-e35fdfd6fa5b44ae863c72648a960a092025-08-20T03:25:04ZengElsevierEnergy Strategy Reviews2211-467X2025-05-015910175710.1016/j.esr.2025.101757A new grey seasonal multivariate forecasting model and its application in energy consumptionJianming Jiang0Yandong Ban1Ming Zhang2Chiwen Qu3School of Humanities and Management, Youjiang Medical University for Nationalities, Baise, 533000, ChinaSchool of Public Health, Youjiang Medical University for Nationalities, Baise, 533000, ChinaSchool of Humanities and Management, Youjiang Medical University for Nationalities, Baise, 533000, China; Corresponding author.School of Humanities and Management, Youjiang Medical University for Nationalities, Baise, 533000, ChinaAccurate energy consumption forecasting is of critical importance, as it enables governments, industries, and individuals to effectively plan for energy supply and demand, thereby reducing the risks associated with both oversupply and shortages. While prior research has explored various forecasting models, relatively few have specifically addressed the challenges of seasonal forecasting under multivariate conditions. To bridge this gap, this paper proposes a novel model—the Discrete Nonlinear Grey Bernoulli Model (DNGBM). At the heart of DNGBM is a set of dynamic parameters specifically designed to capture seasonal variation, making the model well-suited for periodic energy consumption patterns. The model also introduces nonlinear parameters that broaden its representational capacity, thereby enhancing modeling flexibility and accuracy. This dual enhancement enables DNGBM to outperform traditional grey models in both predictive precision and adaptability across diverse energy forecasting scenarios. In the two provided cases, the new model's average results for MAPE, MAE, MSE, and R2 are 3.664 %, 65.596, 11182.902, and 0.952, respectively, outperforming other competing models, which validates its effectiveness.http://www.sciencedirect.com/science/article/pii/S2211467X25001208Energy consumption forecastingGrey prediction modelArtificial protozoa optimizer |
| spellingShingle | Jianming Jiang Yandong Ban Ming Zhang Chiwen Qu A new grey seasonal multivariate forecasting model and its application in energy consumption Energy Strategy Reviews Energy consumption forecasting Grey prediction model Artificial protozoa optimizer |
| title | A new grey seasonal multivariate forecasting model and its application in energy consumption |
| title_full | A new grey seasonal multivariate forecasting model and its application in energy consumption |
| title_fullStr | A new grey seasonal multivariate forecasting model and its application in energy consumption |
| title_full_unstemmed | A new grey seasonal multivariate forecasting model and its application in energy consumption |
| title_short | A new grey seasonal multivariate forecasting model and its application in energy consumption |
| title_sort | new grey seasonal multivariate forecasting model and its application in energy consumption |
| topic | Energy consumption forecasting Grey prediction model Artificial protozoa optimizer |
| url | http://www.sciencedirect.com/science/article/pii/S2211467X25001208 |
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