Summary of Any-quantile Probabilistic Forecasting Of Short-term Electricity Demand, by Slawek Smyl et al.
Any-Quantile Probabilistic Forecasting of Short-Term Electricity Demand
by Slawek Smyl, Boris N. Oreshkin, Paweł Pełka, Grzegorz Dudek
First submitted to arxiv on: 26 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 tackles the challenge of distributional forecasting in power systems, where uncertainty stems from various factors. Despite recent advancements in point forecasts using deep learning, accurate distributional forecasting remains a hurdle. The authors propose a novel approach for predicting arbitrary quantiles, demonstrating its applicability to two neural architectures and achieving state-of-the-art results in short-term electricity demand forecasting. Empirical validation is performed on 35 hourly time-series datasets from European countries. The proposed method’s code is available online. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us predict how much energy people will use in the future. It’s hard to get it right because many things can affect energy usage, like weather and economic trends. Scientists have made progress with predicting specific numbers, but it’s still tricky to predict a range of possibilities. The researchers came up with a new way to forecast energy usage that works well for different types of predictions. They tested it on data from 35 European countries and showed it does better than previous methods. |
Keywords
» Artificial intelligence » Deep learning » Time series
