Power Systems

Follow this topic Ask about this topic

Momentum

20 papers in the last four weeks, up 122% on the four weeks before. 0.2% of all new papers.

Jul 13Week of Sep 28

Latest papers 149

All topics

Explainable Data-driven Deep Reinforcement Learning Methods for Optimal Energy Management in Buildings
Jun 1, 2026Hallah Shahid Butt, Qiong Huang, Gökhan Demirel +6RL Control Reinforcement Learning
Karlsruhe Institute of Technology, 76344 Eggenstein-Leopoldshafen, Germany
Certificates without Electrons? Theory and Evidence on Impacts from AI-Driven Power Demand
May 30, 2026Dana Golden, Aruna Balasubramanian, Niranjan BalasubramanianPower Systems
Department of Economics, Stony Brook University, Stony Brook, 11794, New York, USA · Department of Computer Science, Stony Brook University, Stony Brook, 11794, New York, USA
Interpretable Policy Distillation for Power Grid Topology Control
May 30, 2026Aleksandra Dmitruka, Karlis FreivaldsRL Control Reinforcement Learning
University of Latvia, Faculty of Exact Sciences and Technology, Riga, Latvia
Learning to Bid in FCR Markets: A Best-of-Both-Worlds Approach
May 29, 2026Marius Potfer, Cheng Wan, Pierre GruetAutomated Bidding Power Systems
ENSAE, CREST (Fairplay joint team), 5 Av. Le Chatelier, 91120, Palaiseau, France · EDF Lab Paris-Saclay, FiME (Laboratoire de Finance des Marchés de l’Énergie), 7 Bd Gaspard Monge, 91120, Palaiseau, France
Cycle-Space Informed Detection of Autoencoded Blind False Data Injection Attacks on Power Systems
May 27, 2026Xin Li, Chenhan Xiao, Jonathan Cohen +3False Data Injection Attacks Cybersecurity
Faculty of Computer and Information Science, Ben-Gurion-University, Be’er Sheva, Israel · School of Electrical, Computer and Energy Engineering, Arizona State University, AZ, USA · Department of Computer Science, Shamoon College of Engineering, Be’er Sheva, Israel
Decision-focused learning for optimal PV-Battery scheduling
May 27, 2026Joris Depoortere, Hussain Kazmi, Johan DriesenSequential Decision Making Time Series Forecasting
ESAT-Electa KU Leuven, Kasteelpark Arenberg 10, Leuven, 3001, Belgium
Outage Detection in Self-Healing Smart Grids Using Reinforcement Learning with Spectral Graph Neural Networks
May 26, 2026Lihui Liu, Mucun Sun, Caisheng WangRL Control Graph Neural Networks
Wayne State University, Detroit, MI, USA
From Accounting to Coordination: A Virtual Water-Aware Electricity-Computation-Water Nexus Framework for Data Center Dispatch
May 25, 2026Haiyang You, Chengwei Lou, Jin Zhao +3Power Systems
Emission-Aware Reinforcement Learning for Sustainable Electric Vehicle Charging and Carbon Dioxide Reduction Under Varying Renewable Penetration
May 23, 2026Ninglin Ou, Mohammad A. Razzaque, Iftekher Islam Shovon +5RL Control Multi-Objective Reinforcement Learning
anasc Research, School of Computer Science & IT, University College Cork, IE · School of Computing, Engineering and Digital Technologies, Teesside University, UK · International Energy Research Centre, Tyndall National Institute, Cork, IE +1
Scalable Heterogeneous Graph Foundation Models for Data-Driven Optimal Power Flow in Smart Grids
May 22, 2026Massimiliano Lupo Pasini, Yijiang Li, Kibaek Kim +1Neural Surrogate Modeling Heterogeneous GNNs
Computational Sciences and Engineering Division, Oak Ridge National Laboratory, 1 Bethel Valley Road, Oak Ridge, TN 37831, USA
Cyber-Physical Anomaly Detection in IoT-Enabled Smart Grids Using Machine Learning and Metaheuristic Feature Optimization
May 21, 2026Adis Alihodžić, Eva Tuba, Milan TubaCyber-Physical Systems Cybersecurity
Department of Mathematical and Computer Sciences, University of Sarajevo, Zmaja od Bosne 33–35, Sarajevo, 71000, Bosnia & Herzegovina · Singidunum University, Danijelova 32, Belgrade, 11000, Serbia · Trinity University, 1 Trinity Pl, San Antonio, 78212, Texas, USA +1
Engineering Hybrid Physics-Informed Neural Networks for Next-Generation Electricity Systems: A State-of-the-Art Review
May 21, 2026Joseph NyangonPhysics-Informed ML Physics-Informed Neural Operators
Energy Exemplar, Salt Lake City, UT, 84111, USA
PeakFocus: Bridging Peak Localization and Intensity Regression via a Unified Multi-Scale Framework for Electricity Load Forecasting
May 20, 2026Wangzhi Yu, Peng Zhu, Qing Zhao +2Time Series Forecasting Electricity Load Forecasting
School of Computer Science and Technology, Tongji University, Shanghai, China · Big Data Center, State Grid Corporation of China, Beijing, China
Long-term Power Grid Planning via Answer Set Programming
May 19, 2026Antonio Ielo, Francesco Doria, Sandra Castellanos-Paez +3Long-Horizon Planning Power Systems
University of Calabria, Italy · University of Huddersfield, UK
Latency-Aware Deep Learning Benchmark for Real-Time Cyber-Physical Attack and Fault Classification in Inverter-Dominated Power Grids
May 17, 2026Emad Abukhousa, Saman Zonouz, A. P. Sakis MeliopoulosEfficient Neural Network Inference Time Series Classification
School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA
Empirical evaluation of Time Series Foundation Models for Day-ahead and Imbalance Electricity Price Forecasting in Belgium
May 16, 2026Chi Bui, Maria Margarida Mascarenhas, Arnaud Verstraeten +1Zero-Shot Time Series Forecasting Electricity Price Forecasting
ELECTA-ESAT (KU Leuven) Leuven, Belgium · ELECTA-ESAT(KU Leuven) and Gridual Leuven, Belgium
GenAI-FDIA: Physics-Informed Generative Models for False Data Injection Attacks
May 15, 2026Mohammad A. Razzaque, Muta Tah HiraCyber-Physical Systems Physics-Informed Generative Modeling
School of Computing, Engineering and Digital Technologies, Teesside University, UK · Smartifier Ltd, Stockton-on-Tees, UK
Designing Datacenter Power Delivery Hierarchies for the AI Era
May 15, 2026Grant Wilkins, Fiodar Kazhamiaka, Alok Gautam Kumbhare +2Power Systems
Stanford University Stanford, CA, USA · Microsoft Azure Research Redmond, WA, USA
Quantifying Cyber-Vulnerability in Power Electronics Systems via an Impedance-Based Attack Reachable Domain
May 14, 2026Hongwei Zhen, Ze Yu, Xin Xiang +2Cyber-Physical Systems Reachability Analysis
Grid-Orch: An LLM-Powered Orchestrator for Distribution Grid Simulation and Analytics
May 12, 2026Boming Liu, Jin Dong, Jamie LianLLM Agent Orchestration LLM Tool Use
Electrification and Energy Infrastructures Division, Oak Ridge National Laboratory, Oak Ridge, TN 37830 USA
Towards Affordable Energy: A Gymnasium Environment for Electric Utility Demand-Response Programs
May 12, 2026Jose E. Aguilar Escamilla, Lingdong Zhou, Xiangqi Zhu +1RL Benchmarks Reinforcement Learning
Department of Electrical Engineering and Computer Science, Oregon State University, USA
Multi-market value-stacking: Battery control for combined imbalance participation and non-uniform FCR bidding
May 12, 2026Celle Hendrickx, Fabio Pavirani, Chris DevelderPower Systems
Gent University - imec, IDLab · Ghent, Belgium
ECTO: Exogenous-Conditioned Temporal Operator for Ultra-Short-Term Wind Power Forecasting
May 12, 2026Cao Yuan, Junjun WangTime Series Forecasting Non-Stationary Time Series Forecasting
Wuhan Polytechnic University, Wuhan, 430048, Hubei, China
Newton's Lantern: A Reinforcement Learning Framework for Finetuning AC Power Flow Warm Start Models
May 11, 2026Shourya Bose, Helgi Hilmarsson, Dhruv SuriRL Fine-Tuning Power Systems
Pravah
TokaMind for Power Grid: Cross-Domain Transfer from Fusion Plasma
May 10, 2026JC Wu, Norton Lee, Kai Siang ChenTime Series Classification Cross-Domain Transfer Learning
TaiScience Research Group, affiliated with Fu Jen Catholic University, Taiwan · Center for Geometry and Physics, Institute for Basic Science (IBS), South Korea · Fu Jen Catholic University, Taiwan
A Market-Rule-Informed Neural Network for Efficient Imbalance Electricity Price Forecasting
May 9, 2026Runyao Yu, Julia Lin, Derek W. Bunn +6Electricity Price Forecasting Time Series Forecasting
Delft University of Technology · Austrian Institute of Technology · London Business School +2
A Quantum Inspired Variational Kernel and Explainable AI Framework for Cross Region Solar and Wind Energy Forecasting
May 9, 2026Pavan Manjunath, Thomas PruferTime Series Forecasting Quantum-Inspired ML
Independent Research Scholar, Germany
Inductive Power Grid Cascading Failure Analysis with GRU-Gated Graph Attention
May 7, 2026Tianxin Zhou, Xiang Li, Haibing LuGraph Attention Networks Graph Representation Learning
Department of Computer Science & Engineering, Santa Clara University, Santa Clara, USA · Dept. of Information Systems & Analytics, Santa Clara University, Santa Clara, USA
WARP: A Benchmark for Primal-Dual Warm-Starting of Interior-Point Solvers
May 7, 2026Dhruv Suri, Helgi Hilmarsson, Shourya BosePrimal-Dual Optimization Graph Neural Networks
Pravah
OpenG2G: A Simulation Platform for AI Datacenter-Grid Runtime Coordination
May 6, 2026Jae-Won Chung, Zhirui Liang, Yanyong Mao +3Demand Response Power Systems
University of Michigan

Jun 1, 2026, cs.AIJ/K move · Enter open · S save

Explainable Data-driven Deep Reinforcement Learning Methods for Optimal Energy Management in Buildings

Hallah Shahid Butt, Qiong Huang, Gökhan Demirel, Kevin Förderer+5

Karlsruhe Institute of Technology, 76344 Eggenstein-Leopoldshafen, Germany

RL Control Reinforcement Learning Building Energy Management Interpretability in RL Power Systems

Open paper AskPDF arXiv

The increasing integration of renewable energy sources into power systems, particularly in buildings equipped with photovoltaic (PV) panels and energy storage systems, introduces significant complexity in energy systems. Volatile power generation, varying electricity tariffs, and increased entities, e.g., PV systems, and heat pumps, have increased the complexity and made the system harder to operate. This leads to the demand for additional control and optimization routes including data-based controls, such as reinforcement learning. While deep reinforcement learning (DRL) has emerged as a promising solution to optimize building operations in dynamic and ever more complex environments, its black-box nature impedes user trust and practical adoption. This paper presents a framework for explainable deep reinforcement learning (XRL) applied to energy management in residential buildings. We demonstrate its usage on both synthetic data but also on real-world data from the Living Lab Energy Campus (LLEC) at KIT. We train and compare both on-policy and off-policy DRL agents on an expanded state space that incorporates real-time measurements (demand, PV generation, battery power, state of charge), external signals (dynamic electricity price, local weather data), calendrical and holiday indicators, and forecasts for demand and price. Our experimental results indicate that on-policy algorithms, particularly Advantage Actor Critic (A2C) and Proximal Policy Optimization (PPO), outperform off-policy methods in terms of cumulative rewards and policy stability. To explain these models, we employ post-hoc interpretation techniques to elaborate the learned control policies. Our findings demonstrate that the XRL framework not only reduces electricity costs through optimal battery management, but also provides transparent, actionable insights into the agent's decision-making process.

Momentum

Related topics

Latest papers 149