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Talk to Your Data: An Agentic Artificial Intelligence-Driven Decision-Support Framework for Prosumer Energy Optimization and Recommendations
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DOI：10.1002/aisy.70442.png)
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Abstract
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The growth of distributed renewables and residential storage has turned consumers into active prosumers, creating new energy management challenges that require advanced optimization and transparent support. This study proposes an agentic AI-driven decision-support framework for load optimization and behavioral guidance that integrates models for photovoltaic (PV) generation and load profiling, short-term operational optimization, long-term system planning and a comprehensive performance evaluation layer based on energy, economic and battery health KPIs. These components are orchestrated with a four-agent architecture, comprising a Profiler, a Short-Term Advisor, a Long-Term Advisor, and a Q&A Agent that combine large language models (LLM) with explicit rule-based policies to produce reliable, interpretable and economic recommendations for energy management. Short-term optimization significantly reduces grid imports by aligning flexible loads with PV production and stabilizing battery behavior. Long-term optimization achieves near-autonomous system configurations, increasing self-sufficiency from 69.6% to 97.55% for Prosumer P1 and from 37.2% to 72.4% for Prosumer P2, while cutting grid dependence by more than half. The advisory agents successfully prevent unnecessary system oversizing, enforce battery protection and generate economic recommendations. The Q&A agent achieves high performance, with intent accuracy above 96%, numerical correctness above 93% and high user-rated usefulness, confirming the framework's accessibility for nontechnical users.
Keywords:
agentic artificial intelligence
energy analytics
large language model
natural language
optimization
photovoltaic-battery systems
prosumer
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