Explainable Artificial Intelligence (XAI) techniques for energy and power systems: Review, challenges and opportunities

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Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI)

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XAI—Explainable artificial intelligence

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An Overview of Artificial Intelligence Applications for Power Electronics

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Energy-Sharing Model With Price-Based Demand Response for Microgrids of Peer-to-Peer Prosumers

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Opportunities and Challenges in Explainable Artificial Intelligence (XAI): A Survey

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Deep-Reinforcement-Learning-Based Autonomous Voltage Control for Power Grid Operations

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Data-Driven Load Frequency Control for Stochastic Power Systems: A Deep Reinforcement Learning Method With Continuous Action Search

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Artificial Intelligence Techniques for Stability Analysis and Control in Smart Grids: Methodologies, Applications, Challenges and Future Directions

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Dynamic Pricing and Energy Consumption Scheduling With Reinforcement Learning

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Disaggregated End-Use Energy Sensing for the Smart Grid

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Double Deep $Q$ -Learning-Based Distributed Operation of Battery Energy Storage System Considering Uncertainties

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Deep learning methods and applications for electrical power systems: A comprehensive review

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XGBoost-Based Algorithm Interpretation and Application on Post-Fault Transient Stability Status Prediction of Power System

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Deep reinforcement learning for home energy management system control

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Gaining Insight Into Solar Photovoltaic Power Generation Forecasting Utilizing Explainable Artificial Intelligence Tools

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Attention-based interpretable neural network for building cooling load prediction

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A Survey of Data-driven and Knowledge-aware eXplainable AI

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Detection of power grid disturbances and cyber-attacks based on machine learning

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Demand-Side Management Using Deep Learning for Smart Charging of Electric Vehicles

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Deep learning in power systems research: A review

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An interpretable probabilistic model for short-term solar power forecasting using natural gradient boosting

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A novel methodology to explain and evaluate data-driven building energy performance models based on interpretable machine learning

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Real-Time Faulted Line Localization and PMU Placement in Power Systems Through Convolutional Neural Networks

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EnergyStar++: Towards more accurate and explanatory building energy benchmarking

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Review on Deep Neural Networks Applied to Low-Frequency NILM

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Scenario-based prediction of climate change impacts on building cooling energy consumption with explainable artificial intelligence

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Explainable AI in Deep Reinforcement Learning Models for Power System Emergency Control

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Interpretable deep learning model for building energy consumption prediction based on attention mechanism

Gao, Y., & Ruan, Y. (2021) · Energy and Buildings · doi:10.1016/j.enbuild.2021.111379 · Cited by 131 · see citing works

Machine Learning for Sustainable Energy Systems

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Improved Deep Belief Network and Model Interpretation Method for Power System Transient Stability Assessment

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Explainable long-term building energy consumption prediction using QLattice

Wenninger, S., Kaymakci, C., & Wiethe, C. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.118300 · Cited by 107 · see citing works

Solar irradiance forecasting based on direct explainable neural network

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From Optimization-Based Machine Learning to Interpretable Security Rules for Operation

Cremer, J. L., Konstantelos, I., & Štrbac, G. (2019) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2019.2911598 · Cited by 100 · see citing works

Explainable AI in Deep Reinforcement Learning Models: A SHAP Method Applied in Power System Emergency Control

Zhang, K., Xu, P., & Zhang, J. J. (2020) · IEEE Conference on Energy Internet and Energy System Integration (EI2) · doi:10.1109/ei250167.2020.9347147 · Cited by 100 · see citing works

A machine-learning approach to predicting Africa’s electricity mix based on planned power plants and their chances of success

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Current state of neural networks applications in power system monitoring and control

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Revealing drivers and risks for power grid frequency stability with explainable AIPreprint

Kruse, J., Sch\"afer, B., & Witthaut, D. (2021) · arXiv (Cornell University) · doi:10.1016/j.patter.2021.100365 · Cited by 78 · see citing works

2023 IEEE the 7th Conference on Energy Internet and Energy System Integration

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An Interpretable Deep Learning Method for Power System Transient Stability Assessment via Tree Regularization

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Measuring Explainability and Trustworthiness of Power Quality Disturbances Classifiers Using XAI—Explainable Artificial Intelligence

Machlev, R., Perl, M., Belikov, J., et al. (2021) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2021.3126111 · Cited by 69 · see citing works

An Interpretable Machine Learning Model for Daily Global Solar Radiation Prediction

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Interpretable Probabilistic Forecasting of Imbalances in Renewable-Dominated Electricity Systems

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A bi-level machine learning method for fault diagnosis of oil-immersed transformers with feature explainability

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Hourly performance forecast of a dew point cooler using explainable Artificial Intelligence and evolutionary optimisations by 2050

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Edge-based Explainable Fault Detection Systems for photovoltaic panels on edge nodes

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What's in the box?! Towards explainable machine learning applied to non-residential building smart meter classification

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Proceedings of the 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation

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An Interpretable Solar Photovoltaic Power Generation Forecasting Approach Using An Explainable Artificial Intelligence Tool

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Detection of Synchrophasor False Data Injection Attack Using Feature Interactive Network

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Secondary control activation analysed and predicted with explainable AI

Kruse, J., Schäfer, B., & Witthaut, D. (2022) · Electric Power Systems Research · doi:10.1016/j.epsr.2022.108489 · Cited by 29 · see citing works

Interpretable Machine Learning In Sustainable Edge Computing: A Case Study of Short-Term Photovoltaic Power Output Prediction

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Neural network interpretability for forecasting of aggregated renewable generation

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On the Explainability of Black Box Data-Driven Controllers for Power Electronic Converters

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Reinforcement learning for the unit commitment problem

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Non-invasive load-shed authentication model for demand response applications assisted by event-based non-intrusive load monitoring

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Performance analysis of a DNN classifier for power system events using an interpretability method

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A Novel Graphical Lasso Based Approach Towards Segmentation Analysis in Energy Game-Theoretic Frameworks

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Explainable NILM Networks

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Interpretation of Load Forecasting Using Explainable Artificial Intelligence Techniques

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Explainable Clustering Using Hyper-Rectangles for Building Energy Simulation Data

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Design, Benchmarking and Explainability Analysis of a Game-Theoretic Framework towards Energy Efficiency in Smart Infrastructure

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Explanation for building energy prediction

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Interpretation of Stability Assessment Machine Learning Models Based on Shapley Value

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A Novel Interpretable Model for Solar Radiation Prediction based on Adaptive Fuzzy Clustering and Linguistic Hedges

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Explainable Incipient Fault Detection Systems for Photovoltaic PanelsPreprint

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Secondary control activation analysed and predicted with explainable AI

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Interpreting Black-Box Models: A Review on Explainable Artificial Intelligence

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A literature review on applications of explainable artificial intelligence (XAI)

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AI explainability and governance in smart energy systems: A review

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Artificial intelligence integrated grid systems: Technologies, potential frameworks, challenges, and research directions

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Utilization and challenges of artificial intelligence in the energy sector

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Explainable Anomaly Detection Based on Operational Sequences in Industrial Control Systems

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Donors’ influence strategies and beneficiary accountability: an NGO case study

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