Bridging Natural Language and Microgrid Dynamics: A Context-Aware Simulator and Dataset

2026-04-07

AI Summary

This work addresses the limitation of conventional renewable energy management systems, which predominantly rely on numerical time series while overlooking critical predictive signals embedded in human-generated unstructured textual context—such as schedules, logs, and user intent. To bridge this gap, we propose OpenCEM, the first open-source digital twin platform that uniquely aligns and fuses linguistic context with dynamic microgrid data, establishing a modular, multimodal, context-aware simulation architecture. OpenCEM natively supports integration with large language models and synergistically combines data-driven approaches with physics-based modeling for load forecasting and battery charge–discharge control. Using a high-fidelity simulation environment and a real-world photovoltaic–battery microgrid dataset enriched with linguistic context, we demonstrate that context-aware methods significantly enhance prediction accuracy and improve online battery dispatch strategies.

Abstract

Addressing the critical need for intelligent, context-aware energy management in renewable systems, we introduce the \textbf{OpenCEM Simulator and Dataset}: the first open-source digital twin explicitly designed to integrate rich, unstructured contextual information with quantitative renewable energy dynamics. Traditional energy management relies heavily on numerical time series, thereby neglecting the significant predictive power embedded in human-generated context (e.g., event schedules, system logs, user intentions). OpenCEM bridges this gap by offering a unique platform comprising both a meticulously aligned, language-rich dataset from a real-world PV-and-battery microgrid installation and a modular simulator capable of natively processing this multi-modal context. The OpenCEM Simulator provides a high-fidelity environment for developing and validating novel control algorithms and prediction models, particularly those leveraging Large Language Models. We detail its component-based architecture, hybrid data-driven and physics-based modelling capabilities, and demonstrate its utility through practical examples, including context-aware load forecasting and the implementation of online optimal battery charging control strategies. By making this platform publicly available, OpenCEM aims to accelerate research into the next generation of intelligent, sustainable, and truly context-aware energy systems.

Problem

• context-aware energy management
• microgrid dynamics
• unstructured contextual information
• renewable energy systems
• digital twin

Innovation

• context-aware energy management
• digital twin
• multimodal simulation
• large language models
• microgrid dynamics