Natural Language-Driven Analytics for Networked Photovoltaic Systems Using LLM-Based Agents

Abstract

The analysis of photovoltaic (PV) degradation data presents significant challenges due to the complexity and volume of heterogeneous information generated by modern monitoring systems. In contemporary deployments, PV installations operate as distributed, networked cyber-physical systems, where multiple sensing devices and monitoring nodes continuously generate multi-modal data streams. This paper proposes a natural language-driven approach based on Large Language Model (LLM) agents to enhance the accessibility of analytics in such networked environments. We design an agent-based architecture that translates natural language intents into executable analytical workflows, enabling intuitive interaction with data generated by IoT-enabled PV monitoring infrastructures. The system integrates LLM reasoning with structured data processing, visualization tools, and domain-specific context. The proposed approach is implemented using Python and LangChain, and evaluated on the PV EL HDR BW Test DB dataset. Experimental results demonstrate an overall success rate of 83.3% across representative analytical tasks, highlighting the practical viability of LLM-based agents for domain-specific data analysis. This work contributes to AI-driven analytics for distributed and networked systems, showing how natural language interfaces can support data-driven decision-making in emerging intelligent energy and IoT infrastructures.