Bridging Natural Language and Microgrid Dynamics: A Context-Aware Simulator and Dataset
Tinko Sebastian Bartels, Ruixiang Wu, Xinyu Lu, Yikai Lu, Fanzeng Xia, Haoxiang Yang, Yue Chen, Tongxin Li

TL;DR
This paper introduces OpenCEM, an open-source digital twin platform combining a rich dataset and a simulator for context-aware energy management in renewable microgrids, enabling advanced AI-driven control and prediction.
Contribution
It presents the first platform integrating unstructured contextual data with renewable energy dynamics, facilitating development of intelligent, context-aware energy management algorithms.
Findings
OpenCEM dataset captures real-world PV-and-battery microgrid context.
The simulator supports multi-modal data processing and physics-based modeling.
Practical examples demonstrate improved load forecasting and battery control.
Abstract
Addressing the critical need for intelligent, context-aware energy management in renewable systems, we introduce the 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…
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