So, that was 2 years deeper into the deep learning revolution, post-DeepMind acquisition, doesn't actually say it was a NN (the diagram could be literally any ML model from linear model to random forest), doesn't say they reduced costs by anything approaching 40%, or even are using it in production at all aside from the one instance they patched around some downtime.

"Today we’re releasing a white paper (PDF) on how we’re using neural networks to optimize data center operations and drive our energy use to new lows."

Ah, missed that. In any case, the paper confirms what I said: they haven't used it in practice, and the only time they have was the brief one mentioned in the post where it resulted in a small PUE saving (it quotes 0.02, off an unspecified reduced load but note for comparison the average PUE of ~1.12, so saving anything remotely like 40% is unlikely).

Here is a followup from the same lead author of the paper referred to in that first blog post (Jim Gao) who apparently was involved in Deepmind's project. Note the conspicuous lack of any sort of reference to deep reinforcement learning

https://blog.google/topics/environment/deepmind-ai-reduces-e...

Using forecasting for 'control' doesn't make too much sense (why the need to train a second ensemble to prevent overshoot if it's just supervised learning?), and the first author on that post, is not Gao but Richard Evans who is a DeepMind deep RL researcher (most recent publications: "Deep Reinforcement Learning in Large Discrete Action Spaces", "Deep Reinforcement Learning with Attention for Slate Markov Decision Processes with High-Dimensional States and Actions", "Reinforcement Learning in a Neurally Controlled Robot Using Dopamine Modulated STDP").

Misremembered the year. Actually 2014