mlsanityData to 7 Oct 2026

Why Reinforcement Learning in Energy Systems Needs Explanations

arXiv 2405.18823 ↗First posted 29 May 2024cs.AI cs.SY eess.SYcited by 1

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Abstract (arXiv)

With economic development, the complexity of infrastructure has increased drastically. Similarly, with the shift from fossil fuels to renewable sources of energy, there is a dire need for such systems that not only predict and forecast with accuracy but also help in understanding the process of predictions. Artificial intelligence and machine learning techniques have helped in finding out wellperforming solutions to different problems in the energy sector. However, the usage of state-of-the-art techniques like reinforcement learning is not surprisingly convincing. This paper discusses the application of reinforcement techniques in energy systems and how explanations of these models can be helpful

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 13 of its 44 references that are in the map. First posted on arXiv 29 May 2024.

Builds on

13 of 44 references.

References · 13

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,911
  3. 2018Explaining Explanations: An Overview of Interpretability of Machine Learningcited by 508
  4. 2019Tackling Climate Change with Machine Learningcited by 264
  5. 2024Reinforcement learningcited by 236
  6. 2020Reviewing the Need for Explainable Artificial Intelligence (xAI)cited by 29
  7. 2019Adaptive Power System Emergency Control using Deep Reinforcement Learningcited by 26
  8. 2021The human-AI relationship in decision-making: AI explanation to support people on justifying their decisionscited by 9
  9. 2023Optimal Energy System Scheduling Using A Constraint-Aware Reinforcement Learning Algorithmcited by 9
  10. 2019A Novel Graphical Lasso based approach towards Segmentation Analysis in Energy Game-Theoretic Frameworkscited by 8
  11. 2019Design, Benchmarking and Explainability Analysis of a Game-Theoretic Framework towards Energy Efficiency in Smart Infrastructurecited by 8
  12. 2021An Interpretable Probabilistic Model for Short-Term Solar Power Forecasting Using Natural Gradient Boostingcited by 6
1 more
  1. 2023Regulatory Changes in Power Systems Explored with Explainable Artificial Intelligencecited by 1

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