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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2505.00348 |
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| _version_ | 1866910924446629888 |
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| author | Asghar, Ehtisham Hill, Martin Sengor, Ibrahim Lynch, Conor An, Phan Quang |
| author_facet | Asghar, Ehtisham Hill, Martin Sengor, Ibrahim Lynch, Conor An, Phan Quang |
| contents | Accurate household electrical energy demand prediction is essential for effectively managing sustainable Energy Communities. Integrated with the Energy Management System, these communities aim to optimise operational costs. However, most existing forecasting models are region-specific and depend on large datasets, limiting their applicability across different climates and geographical areas. These models often lack flexibility and may not perform well in regions with limited historical data, leading to inaccurate predictions. This paper proposes a global model for 24-hour-ahead hourly electrical energy demand prediction that is designed to perform effectively across diverse climate conditions and datasets. The model's efficiency is demonstrated using data from two distinct regions: Ireland, with a maritime climate and Vietnam, with a tropical climate. Remarkably, the model achieves high accuracy even with a limited dataset spanning only nine months. Its robustness is further validated across different seasons in Ireland (summer and winter) and Vietnam (dry and wet). The proposed model is evaluated against state-of-the-art machine learning and deep learning methods. Simulation results indicate that the model consistently outperforms benchmark models, showcasing its capability to provide reliable forecasts globally, regardless of varying climatic conditions and data availability. This research underscores the model's potential to enhance the efficiency and sustainability of Energy Communities worldwide. The proposed model achieves a Mean Absolute Percentage Error of 8.0% and 4.0% on the full Irish and Vietnamese datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_00348 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Validation of a 24-hour-ahead Prediction model for a Residential Electrical Load under diverse climate Asghar, Ehtisham Hill, Martin Sengor, Ibrahim Lynch, Conor An, Phan Quang Machine Learning Accurate household electrical energy demand prediction is essential for effectively managing sustainable Energy Communities. Integrated with the Energy Management System, these communities aim to optimise operational costs. However, most existing forecasting models are region-specific and depend on large datasets, limiting their applicability across different climates and geographical areas. These models often lack flexibility and may not perform well in regions with limited historical data, leading to inaccurate predictions. This paper proposes a global model for 24-hour-ahead hourly electrical energy demand prediction that is designed to perform effectively across diverse climate conditions and datasets. The model's efficiency is demonstrated using data from two distinct regions: Ireland, with a maritime climate and Vietnam, with a tropical climate. Remarkably, the model achieves high accuracy even with a limited dataset spanning only nine months. Its robustness is further validated across different seasons in Ireland (summer and winter) and Vietnam (dry and wet). The proposed model is evaluated against state-of-the-art machine learning and deep learning methods. Simulation results indicate that the model consistently outperforms benchmark models, showcasing its capability to provide reliable forecasts globally, regardless of varying climatic conditions and data availability. This research underscores the model's potential to enhance the efficiency and sustainability of Energy Communities worldwide. The proposed model achieves a Mean Absolute Percentage Error of 8.0% and 4.0% on the full Irish and Vietnamese datasets. |
| title | Validation of a 24-hour-ahead Prediction model for a Residential Electrical Load under diverse climate |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2505.00348 |