Summary of Autopq: Automating Quantile Estimation From Point Forecasts in the Context Of Sustainability, by Stefan Meisenbacher et al.
AutoPQ: Automating Quantile estimation from Point forecasts in the context of sustainability
by Stefan Meisenbacher, Kaleb Phipps, Oskar Taubert, Marie Weiel, Markus Götz, Ralf Mikut, Veit Hagenmeyer
First submitted to arxiv on: 30 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Signal Processing (eess.SP); Machine Learning (stat.ML)
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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 In this paper, researchers introduce AutoPQ, a novel method for optimizing probabilistic forecasting for smart grid applications. The key challenges in designing these models are accurately quantifying uncertainty, reducing workload for data scientists, and limiting environmental impact. To address these issues, AutoPQ uses a conditional Invertible Neural Network (cINN) to generate quantile forecasts from existing point forecasts. It also automates the selection of underlying point forecasting methods and optimizes hyperparameters. This allows for flexibility in adapting to different performance needs and computing power. The paper shows that AutoPQ outperforms state-of-the-art methods while limiting computational effort and environmental impact. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AutoPQ is a new way to make predictions about the smart grid, which helps us manage our energy usage more effectively. The big idea is to create forecasts that are not just one number but a range of possibilities. This helps us prepare for different scenarios and makes sure we’re using our resources wisely. AutoPQ does this by using special computer models that can learn from past data and make better predictions. It also helps reduce the amount of energy needed to train these models, which is good news for the environment. |
Keywords
» Artificial intelligence » Neural network
