# Trustworthy artificial intelligence in the energy sector: Landscape analysis and evaluation framework

arXiv: https://arxiv.org/abs/2412.07782 · HTML: https://mlsanity.com/paper/2412.07782/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 65 references that are in the map.
- First posted on arXiv 25 Nov 2024.

## Builds on (most cited first, 19 of 19)

- [Concrete Problems in AI Safety](https://mlsanity.com/paper/1606.06565/) (2016)
- [Datasheets for Datasets](https://mlsanity.com/paper/1803.09010/) (2018)
- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://mlsanity.com/paper/1912.09363/) (2019)
- [N-BEATS: Neural basis expansion analysis for interpretable time series forecasting](https://mlsanity.com/paper/1905.10437/) (2019)
- [Quantifying the Carbon Emissions of Machine Learning](https://mlsanity.com/paper/1910.09700/) (2019)
- [AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias](https://mlsanity.com/paper/1810.01943/) (2018)
- [Sustainable AI: Environmental Implications, Challenges and Opportunities](https://mlsanity.com/paper/2111.00364/) (2021)
- [Adversarial Robustness Toolbox v1.0.0](https://mlsanity.com/paper/1807.01069/) (2018)
- [Trustworthy AI: From Principles to Practices](https://mlsanity.com/paper/2110.01167/) (2021)
- [Aequitas: A Bias and Fairness Audit Toolkit](https://mlsanity.com/paper/1811.05577/) (2018)
- [The role of artificial intelligence in achieving the Sustainable Development Goals](https://mlsanity.com/paper/1905.00501/) (2019)
- [Themis-ml: A Fairness-aware Machine Learning Interface for End-to-end Discrimination Discovery and Mitigation](https://mlsanity.com/paper/1710.06921/) (2017)
- [A comparative assessment of deep learning models for day-ahead load forecasting: Investigating key accuracy drivers](https://mlsanity.com/paper/2302.12168/) (2023)
- [In Search of Deep Learning Architectures for Load Forecasting: A Comparative Analysis and the Impact of the Covid-19 Pandemic on Model Performance](https://mlsanity.com/paper/2302.13046/) (2023)
- [Targeted demand response for flexible energy communities using clustering techniques](https://mlsanity.com/paper/2303.00186/) (2023)
- [SearchFromFree: Adversarial Measurements for Machine Learning-based Energy Theft Detection](https://mlsanity.com/paper/2006.03504/) (2020)
- [Avoiding Occupancy Detection from Smart Meter using Adversarial Machine Learning](https://mlsanity.com/paper/2010.12640/) (2020)
- [Transfer learning for day-ahead load forecasting: a case study on European national electricity demand time series](https://mlsanity.com/paper/2310.15555/) (2023)
- [DeepTSF: Codeless machine learning operations for time series forecasting](https://mlsanity.com/paper/2308.00709/) (2023)

## Built on by (most cited first, 2 of 2)

- [Benchmarking Vision Transformers and CNNs for Thermal Photovoltaic Fault Detection with Explainable AI Validation](https://mlsanity.com/paper/2509.07039/) (2025)
- [A Trustworthy By Design Classification Model for Building Energy Retrofit Decision Support](https://mlsanity.com/paper/2504.06055/) (2025)

## Abstract (arXiv)

The present study aims to evaluate the current fuzzy landscape of Trustworthy AI (TAI) within the European Union (EU), with a specific focus on the energy sector. The analysis encompasses legal frameworks, directives, initiatives, and standards like the AI Ethics Guidelines for Trustworthy AI (EGTAI), the Assessment List for Trustworthy AI (ALTAI), the AI act, and relevant CEN-CENELEC standardization efforts, as well as EU-funded projects such as AI4EU and SHERPA. Subsequently, we introduce a new TAI application framework, called E-TAI, tailored for energy applications, including smart grid and smart building systems. This framework draws inspiration from EGTAI but is customized for AI systems in the energy domain. It is designed for stakeholders in electrical power and energy systems (EPES), including researchers, developers, and energy experts linked to transmission system operators, distribution system operators, utilities, and aggregators. These stakeholders can utilize E-TAI to develop and evaluate AI services for the energy sector with a focus on ensuring trustworthiness throughout their development and iterative assessment processes.