LLM models can be huge. Mind-boggling huge. But… we can also have fun with small models.
He works a company that regulates climate installations in buildings (HVAC, heating, ventilation, air conditioning) via the cloud. Buildings use 30% of all energy worldwide. So improving how the HVAC installation is used has a big impact.
A use case: normally you pre-heat rooms so that it is comfy when you arrive. But sometimes the sun quickly warms the room anyway shortly afterwards. Can you not conserve some energy without sacrificing too much comfort?
You could calculate an optimal solution, but “just” measuring every individual room in combination with an AI.
An “edge device” inside the building.
An external API.
The API stores the data in mysql (the room metadata) and influxdb (the timeseries).
A user selects a room and a machine learning model type and a training data set (from historical data).
The software creates a dataset from influxdb, trains the model (pytorch). The trained neural network goes to ONNX (open neural network exchange). The output is stored in minio (S3-compatible object store). Note: all this is internal: no chatgpt or so.
With the business logic these predictions get interpreted and used for steering the heating. Normally you can achieve 3-5% savings.
The actual steering happens locally in the building with a “go” program that reads the ONNX data. It is open source and is called… gonnx :-)
They have a server with 1 GPU, which is enough for training all those models!
My name is Reinout van Rees and I work a lot with Python (programming language) and Django (website framework). I live in The Netherlands and I'm happily married to Annie van Rees-Kooiman.
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