An Explainable Agentic Architecture for Multi-Site Photovoltaic Power Forecasting
TL;DR
An explainable architecture for multi-site photovoltaic forecasting that extends a validated hybrid ensemble pipeline through system-level design and validation on real-world PV systems demonstrates that the architecture maintains competitive forecasting accuracy while ensuring physical validity and transparent model behavior.
Abstract
Photovoltaic (PV) power forecasting is critical for grid stability and renewable energy integration. However, existing approaches prioritize predictive accuracy while neglecting operational requirements: physical validity under noisy data, transparent decision-making, and scalable multi-site deployment. This paper proposes an explainable architecture for multi-site photovoltaic forecasting that extends a validated hybrid ensemble pipeline through system-level design. Forecasting is structured as an explicit reasoning process comprising perception, inference, validation, explanation, and action stages. Each photovoltaic site uses an autonomous Forecasting Unit (FU), deployed as a microservice for scalable and consistent operation across sites. Explainability is achieved through explicit model orchestration, constraint-based validation, and traceable fallback mechanisms. Validation on real-world PV systems demonstrates that the architecture maintains competitive forecasting accuracy while ensuring physical validity and transparent model behavior. All forecasts are operationally applicable with traceable decision-making processes. These results highlight the relevance of architectural organization and an agentic-by-design (deterministic reasoning loop) architecture, based on explicit validation and orchestration mechanisms, for deploying trustworthy photovoltaic forecasting systems in industrial environments.