Demand Response

Follow this topic Ask about this topic

Demand response studies how households, buildings, businesses, and data centers can shift or reduce electricity use when the grid is constrained or prices change. Common approaches include incentive and pricing models, optimization, reinforcement learning, and coordinated control of flexible loads such as HVAC systems, thermal mass, and computing workloads. Researchers evaluate these methods with smart-meter data, building and grid simulations, and increasingly realistic settings that consider household participation and physical delivery. A central challenge is providing reliable grid flexibility without excessive cost, discomfort, privacy loss, or disruption to users.

Latest papers

23 papers4 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

One Agent per Zone: Decentralised Active Inference under Partial Observability for Multi-Zone HVAC Control on Edge Hardware
Oct 14, 2026Panagiotis Kasnesis, Lazaros Toumanidis, Christos Chatzigeorgiou +2Heating, Ventilation, and Air Conditioning Control Building Energy Management
Waldiez P.C., Maroussi, Greece · ThinGenious P.C., Maroussi, Greece · PRAESC AI, Biel, Switzerland
The Limits of Manipulation within Personalized Incentive Policies
Oct 9, 2026Andrea Araldo, Alessa Mayer, Ravi Seshadri +1Demand Response
SAMOVAR, Télécom SudParis, Institut Polytechnique de Paris, 91120 Palaiseau, France · Institut Polytechnique de Paris · Technical University of Denmark +2
HXAI: Hierarchical Privacy-Preserving Explainable AI in Distributed Energy Systems
Oct 1, 2026Poushali Sengupta, Sabita Maharjan, Frank Eliassen +1Explainable Artificial Intelligence Power Systems
Department of Informatics, University of Oslo, 0316 Oslo, Norway
Learning to Price Electricity for Optimal Demand Response
Sep 30, 2026Jing Shang, Mohammad Mehrabi, Xinyang Zhou +3Demand Response Dynamic Pricing
Stanford University · National Laboratory of the Rockies · Prime Coalition +1
CityLearn v3: A Configurable Simulation and Evaluation Framework for Realistic Control Studies of Renewable Energy Communities
Sep 18, 2026Tiago Fonseca, Luis Lino Ferreira, Armando Sousa +2Power Systems Building Energy Management
INESC TEC / Polytechnic of Porto - School of Engineering, Porto, Portugal · FEUP - Faculty of Engineering, University of Porto / INESC TEC, Porto, Portugal · Department of the Built Environment, Building Services, Eindhoven University of Technology, Eindhoven, the Netherlands
PowerSlider: Exploiting Phase Asymmetry for LLM Serving under Demand Response
Aug 22, 2026Yueying Li, Jiayang Chen, Yuanfan Chen +5LLM Serving LLM Inference Scheduling
Cornell University · Microsoft Azure Research · Cornell Tech
EnergyBridge: Benchmarking Household Energy Management, User Participation, and Grid Flexibility
Aug 9, 2026Xudong Wu, Zeqing Wu, Jiarui Zhang +8Human-in-the-Loop Evaluation Building Energy Management
1The University of Hong Kong · 2Tianjin University · 3Beihang University
Strategic Evaluation of Planning Strategies for LLM Agents in Cyber-Physical Systems
Aug 4, 2026J. de Curtò, I. de ZarzàCyber-Physical Systems LLM Planning
Department of Computer Applications in Science and Engineering, BARCELONA Supercomputing Center, Barcelona, Spain · Escuela T´ecnica Superior de Ingenier´ıa (ICAI), Universidad Pontificia Comillas, Madrid, Spain · Human-Centered AI, Data and Software, LUXEMBOURG Institute of Science and Technology, Esch-sur-Alzette, Luxembourg
Does Demand Response Increase Vulnerability to Cyber Attacks by Adversarial Data Modifications?
Jul 7, 2026Clemens Kortmann, Eike CramerElectricity Price Forecasting Adversarial Attacks
Process Systems Engineering (AVT.SVT) RWTH Aachen University · Department of Chemical Engineering Sargent Centre for Process Systems Engineering University College London
Power-Flexible AI Data Centers: A New Paradigm for Grid-Responsive Compute
Jun 23, 2026Chris Williams, Philip Colangelo, Ayse Coskun +15Demand Response Carbon-Aware Computing
Emerald AI · Electric Power Research Institute (EPRI) · National Grid +2
LLM-Mediated Demand Response Coordination in Smart Microgrids
Jun 9, 2026J. de Curtò, I. de ZarzàMulti-Agent Coordination Agent-Based Modeling
Department of Computer Applications in Science & Engineering, BARCELONA Supercomputing Center, 08034 Barcelona, Spain · Escuela T´ecnica Superior de Ingenier´ıa (ICAI), Universidad Pontificia Comillas, 28015 Madrid, Spain · Human Centered AI, Data & Software, LUXEMBOURG Institute of Science and Technology, L-4362 Esch-sur-Alzette, Luxembourg
A Swarm Approach to Public Transit Using On-demand Routing in a Slime-Mold-Inspired Framework
Jun 4, 2026Lindsay Burke, Maxfield Comstock, Jason Graham +3Intelligent Transportation Systems Vehicle Routing
Department of Computer Science, New Jersey Institute of Technology · Federated Department of Biological Sciences, New Jersey Institute of Technology · Department of Mathematics, University of Scranton +1
Addressing Terminal Constraints in Data-Driven Demand Response Scheduling
May 14, 2026Maximilian Bloor, Martha White, Ehecatl Antonio del Rio Chanona +1Hierarchical RL Credit Assignment in RL
Sargent Centre for Process Systems Engineering, Imperial College London, London, SW7 2AZ, UK · Department of Computer Science, University of Alberta, Edmonton, AB, Canada · Department of Computing, Imperial College London, London, SW7 2AZ, UK
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
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
A Generalized Synthetic Control Method for Baseline Estimation in Demand Response Services
Apr 20, 2026Jonas Sievers, Mardavij RoozbehaniSynthetic Control Counterfactual Prediction
Institute for Data Processing and Electronics, Karlsruhe Institute of Technology, 76344 Eggenstein-Leopoldshafen, Germany · Laboratory for Information and Decision Systems, Massachusetts Institute of Technology, Boston, MA 02139, USA
Safe Deep Reinforcement Learning for Building Heating Control and Demand-side Flexibility
Apr 17, 2026Colin Jüni, Mina Montazeri, Yi Guo +3RL Control Building Energy Management
Urban Energy Systems Laboratory, Swiss Federal Laboratories for Materials Science and Technology, Dubendorf, Switzerland. · School of Automation, Beijing Institute of Technology, Haidian District, Beijing, China · Reliability and Risk Engineering Laboratory, ETH Zürich, CH-8092, Switzerland.
An Optimal Battery-Free Approach for Emission Reduction by Storing Solar Surplus in Building Thermal Mass
Mar 30, 2026Michela Boffi, Jessica Leoni, Fabrizio Leonforte +2Building Energy Management Heating, Ventilation, and Air Conditioning Control
Politecnico di Milano, Piazza Leonardo da Vinci, Milano, 20133, Milano, Italy
Hybrid Human-Agent Social Dilemmas in Energy Markets
Mar 12, 2026Isuri Perera, Frits de Nijs, Julian GarciaMulti-Agent Coordination Human-AI Interaction
Department of Data Science and AI, Faculty of Information Technology Monash University, Melbourne, Australia
Conversational Demand Response: Bidirectional Aggregator-Prosumer Coordination through Agentic AI
Mar 6, 2026Reda El Makroum, Sebastian Zwickl-Bernhard, Lukas Kranzl +1Demand Response Multi-Agent Coordination
Energy Economics Group, TU Wien, Gußhausstraße 25–29, Vienna, Austria · Industrial Economics and Technology Management, NTNU, Trondheim, Norway
Capacity-constrained demand response in smart grids using deep reinforcement learning
Feb 18, 2026Shafagh Abband Pashaki, Sepehr Maleki, Amir BadieeDemand Response Constrained RL
Lincoln AI Lab, University of Lincoln, Lincoln, United Kingdom
Calibrated uncertainty quantification for prosumer flexibility aggregation in ancillary service markets
Jan 21, 2026Yogesh Pipada Sunil Kumar, S. Ali Pourmousavi, Jon A. R. Liisberg +1Uncertainty Quantification Uncertainty Calibration
School of Electrical and Mechanical Engineering, The University of Adelaide, Ingkarni Wardli Building, Adelaide, SA 5005, Australia · Watts A/S, Brogade 19D, Køge 4600, Denmark
CROCS: A Two-Stage Clustering Framework for Behaviour-Centric Consumer Segmentation with Smart Meter Data
Jan 15, 2026Luke W. Yerbury, Ricardo J. G. B. Campello, G. C. Livingston +2Clustering Demand Response

Oct 14, 2026J/K move · Enter open · S save

One Agent per Zone: Decentralised Active Inference under Partial Observability for Multi-Zone HVAC Control on Edge Hardware

Panagiotis Kasnesis, Lazaros Toumanidis, Christos Chatzigeorgiou, Amalia Contiero Syropoulou+1

Waldiez P.C., Maroussi, Greece · ThinGenious P.C., Maroussi, Greece · PRAESC AI, Biel, Switzerland

Heating, Ventilation, and Air Conditioning Control Building Energy Management Multi-Agent Control Active Inference Demand Response

Open paper AskPDF arXiv

Multi-zone HVAC control is a shared-resource problem: zones on a floor share one fan and cooling coil, so each zone's demand affects its neighbours and the building's peak power. One controller per zone scales well and isolates faults, but each controller then observes only its own zone, not the shared air loop. In this work, we present a decentralised controller that assigns each zone an independent active-inference agent, which plans by minimising expected free energy and learns its zone dynamics online. The agents exchange no messages, so none depends on peers or a coordinator; each accounts for the unobserved coupling through a latent disturbance and a congestion signal derived from the metered power of the other floors. Fifteen agents are evaluated in EnergyPlus on a Sinergym office building against three rule-based controllers and three MADDPG fleets, one given the same floor meters. On five noisy weather years unseen during training, the fleet attains the lowest mean building peak (88.3 kW, 3 to 17 % below the baselines) and staggers the floor peaks better than any baseline, with a coincidence factor (building peak over the sum of the floor peaks) of 0.929 versus 0.936 to 0.994. Compared with a reactive rule at similar comfort (0.912 versus 0.916), it lowers the sum of monthly peaks, on which demand charges are billed, by 33 kW (4 %) in every year, at the cost of about 10 % more energy. It costs 9% less per year than the proactive rule (at lower comfort) and 1% less than the MADDPG fleet given the same floor meters, in every test year. The whole fleet runs on a single low-cost Raspberry Pi 5, closing the observation-to-action loop over MQTT in 442 ms at p95, far below the 15-minute control step.