Why Reinforcement Learning in Energy Systems Needs Explanations

TL;DR

This paper emphasizes the importance of explainability in reinforcement learning models applied to complex energy systems, highlighting the need for transparent AI solutions to improve understanding and trust.

Contribution

It discusses the application of reinforcement learning in energy systems and advocates for the integration of explanations to enhance model transparency and usability.

Findings

Reinforcement learning models are increasingly used in energy systems.

Explainability can improve trust and decision-making in energy management.

The paper advocates for developing explainable reinforcement learning techniques.

Abstract

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