Summary of Enhanced N-beats For Mid-term Electricity Demand Forecasting, by Mateusz Kasprzyk et al.
Enhanced N-BEATS for Mid-Term Electricity Demand Forecasting
by Mateusz Kasprzyk, Paweł Pełka, Boris N. Oreshkin, Grzegorz Dudek
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The enhanced N-BEATS model, N-BEATS, is designed for improved mid-term electricity load forecasting (MTLF). It builds upon the original N-BEATS architecture’s strengths in handling complex time series data without requiring preprocessing or domain-specific knowledge. The new model introduces a novel loss function combining pinball loss and normalized MSE, and modifies its internal block architecture by adding a destandardization component. Evaluated on real-world monthly electricity consumption data from 35 European countries, N-BEATS outperforms its predecessor and other established forecasting methods in terms of MAPE, RMSE, and dispersion in forecast errors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary N-BEATS* is a new way to predict how much electricity people will use over the next few months. It’s better than previous ways at doing this because it uses a special combination of math problems to figure out what might happen. This helps it make more accurate predictions, which is important for keeping our energy supply stable and reliable. |
Keywords
» Artificial intelligence » Loss function » Mse » Time series
