Google DeepMind-style datacenter optimization AI model (on the cheap)

There was news recently in bloomberg about how google was able to cut electricity usage in its datacenter by using an AI scheme made by DeepMind (of AlphaGo fame). Earlier this week, i decided to make a quick-and-dirty implemetation in python and share it here for anyone interested in a practical example of what exactly they did. First lets take a quick look at why one would want to make such a thing... Datacenters (and indeed any other large scale structures that use a lot of energy) need to be carefully optimized for efficiency as even a 10% - 15% saving on the electricity bill can add up to millions of dollars a year. The biggest challenge here is that even though there are certain simple steps that anyone can take to reduce energy use (don't use a very low server room set-point, use free-cooling when possible, etc…) one can never actually predict quantitatively what the effect of changing variable x by z% will have on total consumption. This is because there simply are too many variables that affect the net consumption of a datacenter (chillers, AHUs, compressors, condensers, fans, outside conditions, latitude, etc…) and its impossible to actually write down a formula that can quantify all these relationships. However, as long as you have a lot of data, ML is perfect for learning complex relationships between multiple features and outcomes.