Summary of Why Reinforcement Learning in Energy Systems Needs Explanations, by Hallah Shahid Butt et al.
Why Reinforcement Learning in Energy Systems Needs Explanations
by Hallah Shahid Butt, Benjamin Schäfer
First submitted to arxiv on: 29 May 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Systems and Control (eess.SY)
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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 explores the application of reinforcement learning (RL) techniques in energy systems, a crucial area given the shift to renewable sources. RL has shown promise in finding well-performing solutions for various problems in the energy sector, but its convincing usage is surprisingly lacking. The authors aim to discuss how RL can be applied effectively in energy systems and provide explanations for these models to enhance their transparency and trustworthiness. Specifically, they investigate the use of reinforcement learning-based approaches for predicting and forecasting energy-related processes, such as demand response and supply chain management. By leveraging AI and ML techniques, the paper contributes to the development of more accurate and transparent energy prediction systems. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using a type of artificial intelligence called reinforcement learning (RL) to improve energy systems. RL helps find good solutions for problems in the energy sector, like predicting how much energy people will use. But right now, RL isn’t being used as well as it could be because we don’t always understand why the models are making certain predictions. The authors want to figure out how RL can be used more effectively and make the models more transparent so people trust them. |
Keywords
» Artificial intelligence » Reinforcement learning
