Summary of Efficient Deterministic Renewable Energy Forecasting Guided by Multiple-location Weather Data, By Charalampos Symeonidis and Nikos Nikolaidis
Efficient Deterministic Renewable Energy Forecasting Guided by Multiple-Location Weather Data
by Charalampos Symeonidis, Nikos Nikolaidis
First submitted to arxiv on: property=“og:description” content=”Electricity generated
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 paper proposes a novel methodology for deterministic wind and solar energy generation forecasting at multiple sites using multi-location weather forecasts. The method employs a U-shaped Temporal Convolutional Auto-Encoder (UTCAE) architecture for temporal processing of weather-related and energy-related time-series across each site. Additionally, the Multi-sized Kernels convolutional Spatio-Temporal Attention (MKST-Attention) mechanism is introduced to efficiently transfer temporal patterns from weather data to energy data without prior knowledge of locations. The proposed method achieves top results in a day-ahead solar and wind energy forecasting scenario on five datasets, outperforming competitive state-of-the-art time-series forecasting methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper suggests a new way to predict when the sun will shine or the wind will blow at different places, using weather forecasts. This helps make sure that the power grid runs smoothly by reducing uncertainty about how much energy is being generated. The method uses special computer architecture and attention mechanism to learn patterns in data from weather stations and solar panels. By doing this, it can predict future energy generation more accurately than other methods. |
Keywords
» Artificial intelligence » Attention » Encoder » Time series
