Master Thesis on What-If Reasoning in LLM Agents for Evaluating HEMS Parameter Effects Using Time-Series Foundation Models via MCP

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How can modern Home Energy Management Systems (HEMS) turn complex user requests into concrete and dependable actions? Large Language Models (LLMs) provide powerful capabilities for dialogue and qualitative reasoning, while specialized Time-Series Foundation Models (TSFMs) bring complementary strengths in capturing and predicting complex numerical dynamics.

In your thesis, you will explore how these technologies can come together for accurate forecasting and closed-loop what-if reasoning – contribute your ideas to intelligent energy management and apply now! During your assignment, you will connect a specialized Time-Series Foundation Model as an independent service to an existing LLM agent ecosystem using the standardized Model Context Protocol (MCP).

You will investigate how numerical time-series predictions generated by the TSFM can be mathematically