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S
Sebastian Zwickl-Bernhard
Energy Economics Group, TU Wien; NTNU
L
Lukas Kranzl
Energy Economics Group, TU Wien
H
Hans Auer
Energy Economics Group, TU Wien; NTNU
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References (20)
[3]
Cost-optimal residential energy scheduling via a zero-shot LLM agent and model predictive control
Founder's Pitch
"Conversational Demand Response system revolutionizing prosumer engagement in residential energy markets through bidirectional natural language AI."
Commercial Viability Breakdown
0-10 scaleHigh Potential
1/4 signals
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4/4 signals
Series A Potential
3/4 signals
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Why It Matters
This research is vital in transforming how energy markets interact with residential prosumers, aiming to achieve critical global demand response targets by enhancing participation through improved transparency and engagement.
Product Angle
The architecture can be packaged as software targeting utilities and energy management companies seeking to enhance their demand response programs and prosumer engagement, offering APIs and tools to integrate with existing energy management platforms.
Disruption
The system potentially replaces conventional one-way demand response communications by establishing dynamic, interactive engagement models that empower prosumers to participate more actively in energy markets.
Product Opportunity
There is a substantial opportunity in residential energy management, with growing pressure to meet climate goals through increased demand response participation. Energy utilities, governments, and consumers who benefit from energy savings and participation incentives would be interested parties.
Use Case Idea
Implement the CDR system as a SaaS for energy companies to improve prosumer participation in demand response programs, enhancing user engagement and optimizing grid reliability.
Science
The paper proposes a multi-agent system for energy management involving prosumers and aggregators. Using agentic AI, it enables two-way communication between the Home Energy Management System (HEMS) and aggregators through natural language, allowing transparent decision-making around energy flexibility and demand response.
Method & Eval
The architecture demonstrates proof-of-concept operations using a multi-agent system with a two-tier setup, tested in scenarios for both aggregator and prosumer-initiated interactions. Simulations show feasibility of real-time communication and decision-making within an acceptable timeframe.
Caveats
Scalability and integration with existing energy markets could be a challenge. The reliance on AI models may also lead to unpredictability in user engagement outcomes. There are potential data privacy issues given the nature of user-home interaction.
Author Intelligence
Reda El Makroum
LEAD Energy Economics Group, TU Wien
elmakroum@eeg.tuwien.ac.at
Sebastian Zwickl-Bernhard
Energy Economics Group, TU Wien; NTNU
Lukas Kranzl
Energy Economics Group, TU Wien
Hans Auer
Energy Economics Group, TU Wien; NTNU