Saved in:
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.05768 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909774468087808 |
|---|---|
| author | Shao, Chen Wang, Yue Zhu, Zhenyi Huang, Zhanbo Pütz, Sebastian Schäfer, Benjamin Käfer, Tobais Färber, Michael |
| author_facet | Shao, Chen Wang, Yue Zhu, Zhenyi Huang, Zhanbo Pütz, Sebastian Schäfer, Benjamin Käfer, Tobais Färber, Michael |
| contents | Energy forecasting is vital for grid reliability and operational efficiency. Although recent advances in time series forecasting have led to progress, existing benchmarks remain limited in spatial and temporal scope and lack multi-energy features. This raises concerns about their reliability and applicability in real-world deployment. To address this, we present the Real-E dataset, covering over 74 power stations across 30+ European countries over a 10-year span with rich metadata. Using Real- E, we conduct an extensive data analysis and benchmark over 20 baselines across various model types. We introduce a new metric to quantify shifts in correlation structures and show that existing methods struggle on our dataset, which exhibits more complex and non-stationary correlation dynamics. Our findings highlight key limitations of current methods and offer a strong empirical basis for building more robust forecasting models |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_05768 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Real-E: A Foundation Benchmark for Advancing Robust and Generalizable Electricity Forecasting Shao, Chen Wang, Yue Zhu, Zhenyi Huang, Zhanbo Pütz, Sebastian Schäfer, Benjamin Käfer, Tobais Färber, Michael Machine Learning Artificial Intelligence Energy forecasting is vital for grid reliability and operational efficiency. Although recent advances in time series forecasting have led to progress, existing benchmarks remain limited in spatial and temporal scope and lack multi-energy features. This raises concerns about their reliability and applicability in real-world deployment. To address this, we present the Real-E dataset, covering over 74 power stations across 30+ European countries over a 10-year span with rich metadata. Using Real- E, we conduct an extensive data analysis and benchmark over 20 baselines across various model types. We introduce a new metric to quantify shifts in correlation structures and show that existing methods struggle on our dataset, which exhibits more complex and non-stationary correlation dynamics. Our findings highlight key limitations of current methods and offer a strong empirical basis for building more robust forecasting models |
| title | Real-E: A Foundation Benchmark for Advancing Robust and Generalizable Electricity Forecasting |
| topic | Machine Learning Artificial Intelligence |
| url | https://arxiv.org/abs/2509.05768 |