# Why Reinforcement Learning in Energy Systems Needs Explanations

arXiv: https://arxiv.org/abs/2405.18823 · HTML: https://mlsanity.com/paper/2405.18823/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 44 references that are in the map.
- First posted on arXiv 29 May 2024.

## Builds on (most cited first, 13 of 13)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Explaining Explanations: An Overview of Interpretability of Machine Learning](https://mlsanity.com/paper/1806.00069/) (2018)
- [Tackling Climate Change with Machine Learning](https://mlsanity.com/paper/1906.05433/) (2019)
- [Reinforcement learning](https://mlsanity.com/paper/2405.10369/) (2024)
- [Reviewing the Need for Explainable Artificial Intelligence (xAI)](https://mlsanity.com/paper/2012.01007/) (2020)
- [Adaptive Power System Emergency Control using Deep Reinforcement Learning](https://mlsanity.com/paper/1903.03712/) (2019)
- [Optimal Energy System Scheduling Using A Constraint-Aware Reinforcement Learning Algorithm](https://mlsanity.com/paper/2305.05484/) (2023)
- [The human-AI relationship in decision-making: AI explanation to support people on justifying their decisions](https://mlsanity.com/paper/2102.05460/) (2021)
- [A Novel Graphical Lasso based approach towards Segmentation Analysis in Energy Game-Theoretic Frameworks](https://mlsanity.com/paper/1910.02217/) (2019)
- [Design, Benchmarking and Explainability Analysis of a Game-Theoretic Framework towards Energy Efficiency in Smart Infrastructure](https://mlsanity.com/paper/1910.07899/) (2019)
- [An Interpretable Probabilistic Model for Short-Term Solar Power Forecasting Using Natural Gradient Boosting](https://mlsanity.com/paper/2108.04058/) (2021)
- [Regulatory Changes in Power Systems Explored with Explainable Artificial Intelligence](https://mlsanity.com/paper/2303.17455/) (2023)

## Built on by (most cited first, 1 of 1)

- [Learning to Run Power Networks: Effective AlphaZero-inspired Topological Control](https://mlsanity.com/paper/2608.14114/) (2026)

## 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