# Testing Local LLMs for Smart Home Heating Control

An experiment replacing standard smart thermostat logic with a local large language model.

- **Source:** [How-To Geek](https://www.howtogeek.com)
- **Published:** 2026-11-01
- **Topic:** [Data & AI](https://brifing.si/c/data-ai)
- **Original article:** <https://www.howtogeek.com/local-llm-control-thermostat-versus-nest-and-ecobee/>
- **On Brifing:** https://brifing.si/s/113fc535-112f-4d25-a174-c32f0d9f3057

## The Experimental Setup

The author integrated a Tado smart heating system into Home Assistant to test LLM-based control. By providing the model with home layout data, room warm-up rates, and occupancy sensor inputs, the system aimed to replace standard thermostat schedules. The goal was to achieve more intuitive heating adjustments based on dynamic daily routines.

## Hardware and Model Selection

Running on a Dell OptiPlex with 16GB of RAM, the author tested several models via Ollama. While models like Granite 4 7B and Nemotron 3 Nano 4B struggled with accuracy and latency, Qwen 3.5 9B emerged as the winner. It achieved a 74% decision accuracy with response times between two and five minutes.

## Shadow Testing Phase

Before granting full control, the author performed shadow testing to validate the model's logic without executing commands. This phase allowed for prompt refinements based on hypothetical scenarios and real historical data. Once the model demonstrated sufficient reasoning capabilities, it was granted manual override authority over the existing Tado heating schedules for four days.

## Operational Failures and Logic

During the four-day trial, the LLM struggled with temporal consistency. On Monday, it delayed bedroom heating despite detecting bedtime signals. Tuesday revealed a lack of state retention, where the model forgot its own plan within thirty minutes. These inconsistencies forced the author to update prompts to force the model to consider previous decisions.

## Contextual Reasoning Challenges

The author tested the LLM's ability to handle schedule changes, such as children being off school. While the model successfully factored the new context into its reasoning, it failed to execute correctly, heating the wrong rooms. This highlighted a significant gap between the model's understanding of instructions and its actual physical control output.

## Energy and Performance Metrics

Despite the logic errors, the LLM effectively managed room temperatures and considered humidity to prevent mold growth. However, energy consumption was higher than standard Tado schedules. While the pre-heat timing based on warm-up rates was accurate, the model frequently failed to correctly identify room occupancy, leading to inefficient heating of empty spaces.

## Hardware Limitations Impact

The author concluded that the primary bottleneck was the limited hardware capacity. Larger, more capable models running on more powerful infrastructure might provide the necessary reasoning to make this system effective. Currently, the local 9B parameter model lacks the consistent decision-making power required to reliably manage a complex home heating environment.

## Future Outlook for Automation

While the experiment proved that LLMs can process sensor data for home automation, they are not yet ready to replace dedicated thermostat logic. Future iterations may focus on using LLMs to suggest schedule adjustments rather than direct control. For now, the author has returned control to the Tado system due to reliability concerns.

## Sources covering this story

- https://www.howtogeek.com/local-llm-control-thermostat-versus-nest-and-ecobee/ — 

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_Summary written by Brifing from the original reporting. Copyright in the original article belongs to its publisher; read the full piece at the link above._