Hello all, I am dealing with a problem statement where I need to optimize the energy consumption of HVAC based on variables like temperature, humidity, occupancy etc. I don’t have a very clear picture of how to tackle this problem statement using machine learning or deep learning. Could someone give me hints or recommend some readings?
Thanks in advance! 
While deep learning is a nice hammer to nail many problems I think that your best bet would be a simple PID controller
You can use regression methods to determine its P, I and D terms.
P.S. My first thought was that you are referring to some kind of H-VAC (Variational Autoencoder)
Thanks @SakvaUA, Thanks for the answer. I’m still figuring a way to find out the optimal controls using regression and as you might have figured out HVAC is Heating, ventilation, and air conditioning
Are you trying to optimize design or control parameters? Because PID is for control.
If you have a rough design then interesting solution would be to optimize its parameters using SGD.
Can you describe your problem further?
Hi @neuralMax, so the aim here is to control the parameters like temperature, humidity, pressure etc. of a chiller unit in HVAC , the data set will have information about the past data which will have time, temperature, humidity etc. and the power consumption at that particular time.
you could use machine learning to predict the power consumption based on the other factors. for this i’d feed all the data into TPOT and let it do its magic. having an algorithm that can predict the power consumption, you may be able to come up with something clever that can reduce this dimension somehow. sorry, i dont know much about this field. i would be interested in your solution once you find one
The goal of the exercise is not to reduce power consumption (you can switch it off and get 0 power consumption), but rather to reduce power consumption while keeping certain parameters (temperature, humidity, etc) within acceptable range. Your temperature/humidity response will depend on numerous parameters like temperature/pressure/humidity/wind outside and inside, power dissipation inside your building, wall temperature, erc, etc, etc. What makes it more complex is that the dynamics of the system depend not only on current set of parameters but also on their recent trends. You’ll probably want to look in the direction of RNNs or 1D convolutional networks.
This problem is outside my experience. Maybe it is solvable with reinforcement learning involving some simulation or real time data.