There is a training phase (1-6 years of weather observations) where the RL-agent trains using the building simulation program. Then I evaluate on 2022 weather data using the same building simulation program with the agent I previously trained. The graph contains real measured values from 2022 (blue line) of the building. The agent is evaluated on the weather data from year 2022.
Yes. The building has a certain inertia, this is something I hope the agent want to learn as well. The 36 hours outdoors temperature forecast is supplied in the observation state so that the agent knows it should preheat the building when forecast temperature is going down to lower the heating peak penalty.
In short, we are trying to use Reinforcement Learning to try to control the heating of a building (district heating) with the input buildings zone temperature, outdoor temperature. To not use the real building during training of the RL-algorithm we are using a building simulation program as an environment.
The building simulation program has inputs:
Outputs from the building simulation program are:
The aim of the RL-algorithm is to make a more efficient control of the buildings district heating use, then the current district heating control function. The primary goal is to make the RL-algorithm peak-shave the district... (read 191 more words →)
I have read some articles, I can have a look again, thanks. Were to much information and possibilities so felt better to talk to a human.