# AI Explainability and Governance in Smart Energy Systems: A Review

arXiv: https://arxiv.org/abs/2211.00069 · HTML: https://mlsanity.com/paper/2211.00069/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 157 references that are in the map.
- First posted on arXiv 24 Oct 2022.

## Builds on (most cited first, 9 of 9)

- [Metrics for Explainable AI: Challenges and Prospects](https://mlsanity.com/paper/1812.04608/) (2018)
- [What Does Explainable AI Really Mean? A New Conceptualization of Perspectives](https://mlsanity.com/paper/1710.00794/) (2017)
- [Trustworthy Federated Learning via Blockchain](https://mlsanity.com/paper/2209.04418/) (2022)
- [A FAIR and AI-ready Higgs boson decay dataset](https://mlsanity.com/paper/2108.02214/) (2021)
- [Regression markets and application to energy forecasting](https://mlsanity.com/paper/2110.03633/) (2021)
- [Machine learning of Kondo physics using variational autoencoders and symbolic regression](https://mlsanity.com/paper/2107.08013/) (2021)
- [Artificial intelligence for Sustainable Energy: A Contextual Topic Modeling and Content Analysis](https://mlsanity.com/paper/2110.00828/) (2021)
- [Learning, Computing, and Trustworthiness in Intelligent IoT Environments: Performance-Energy Tradeoffs](https://mlsanity.com/paper/2110.01686/) (2021)
- [Secondary control activation analysed and predicted with explainable AI](https://mlsanity.com/paper/2109.04802/) (2021)

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

- [Multi-generational labour markets: data-driven discovery of multi-perspective system parameters using machine learning](https://mlsanity.com/paper/2302.10146/) (2023)

## Abstract (arXiv)

Traditional electrical power grids have long suffered from operational unreliability, instability, inflexibility, and inefficiency. Smart grids (or smart energy systems) continue to transform the energy sector with emerging technologies, renewable energy sources, and other trends. Artificial intelligence (AI) is being applied to smart energy systems to process massive and complex data in this sector and make smart and timely decisions. However, the lack of explainability and governability of AI is a major concern for stakeholders hindering a fast uptake of AI in the energy sector. This paper provides a review of AI explainability and governance in smart energy systems. We collect 3,568 relevant papers from the Scopus database, automatically discover 15 parameters or themes for AI governance in energy and elaborate the research landscape by reviewing over 150 papers and providing temporal progressions of the research. The methodology for discovering parameters or themes is based on "deep journalism", our data-driven deep learning-based big data analytics approach to automatically discover and analyse cross-sectional multi-perspective information to enable better decision-making and develop better instruments for governance. The findings show that research on AI explainability in energy systems is segmented and narrowly focussed on a few AI traits and energy system problems. This paper deepens our knowledge of AI governance in energy and is expected to help governments, industry, academics, energy prosumers, and other stakeholders to understand the landscape of AI in the energy sector, leading to better design, operations, utilisation, and risk management of energy systems.