Hi, I’m confused about the timeseries data for each building for training on: the indoor temperatures, indoor RH, and setpoints are all filled in. (1) My understanding is that the indoor temp and RH values should be populated dynamically based in response to the cooling applied from the heat pump in combination with outdoor conditions and current conditions… did I misunderstand? (2) Also the setpoint… why is it included if it is effectively overridden/ignored by the model inside the gym and has no bearing on the evaluation metrics (i.e., PMV/comfort are not a function of the provided setpoints, right)?
Hi @donnavakalis your understanding is correct about (1),
Historically, CityLearn could not control the heat pump and there were no temperature dynamics. Instead, there was the assumption that the ideal cooling/heating loads were always met while using the storage systems to provide load shifting flexibility. In this previous setup, the indoor temperature in the csv files were useful as they were static and didn’t change during runtime. RH values have always been included in the csv files in case the modeler found it useful as an observation but it doesn’t actually get used during simulation so it can be ignored all together.
The updated env we are using this year allows the heat pump to be controlled and can predict the temperature change based on how much energy the heat pump delivers. So the indoor temp at runtime changes from what is in the csv files. However, one might choose to use the dataset in the old set up or the new setup which is why the values are still populated in the csv. Also, the LSTM that does the temperature prediction has a lookback of 12 time steps so the very first 12 time steps of an episodes will use the data in the csv to infer indoor temp.
Regarding your second question, the setpoint in the csv is actually useful in two ways. First, it can be used as an observation to the agent. Secondly, it actually is considered when calculating delta for comfort. See here where it is used to calculate the temperature delta observation, here where it is used to calculate the comfort bands in reward function and here where it is used to calculate delta in the discomfort evaluation metric.