DeepMind in “very early stage” talks with National Grid to reduce UK energy use
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Re: DeepMind in “very early stage” talks with National Grid to reduce UK energy use
#2This is about prediction of supply. In other words, becoming a weather forecasting organization, mainly wind and clouds/shadow.
Demand is predictable so little to gain there.
How big is the market of providing excess demand? If I can turn on the AC or charge electric cars in the millions, what can I earn as a company?
Re: DeepMind in “very early stage” talks with National Grid to reduce UK energy use
#3I doubt this will use deep learning though. DL works best at detecting patterns in large quantities of data, whereas the time series data here is limited to just a few years...and the system has changed over those year, and continues to change significantly. I'd guess that 10% is the cost of ensuring supply, i.e. spinning up generators as contingency - any attempt to shave saving from that may increase risk of power outages. Doing so while maintaining the same power outage risk is the goal here, but I think it would be hard to prove that some new clever strategy has the same risk levels. In this respect 10% sounds ambitious to me.
Re: DeepMind in “very early stage” talks with National Grid to reduce UK energy use
#4This title misses the following words; "suggests they can", "proposes to", or my personal favourite "something something ai solves all your problems"
come on folks, we can be better than this.
Re: DeepMind in “very early stage” talks with National Grid to reduce UK energy use
#5The DeepMind datacenter project was very interesting, but a lot of the ML people I spoke to were quite dubious about how much of it was genuinely down to new AI/neural networks, and how much of it was Google PR to justify how much they spent on DeepMind.
> DeepMind trained a neural network to more accurately predict future cooling requirements, in turn reducing the power usage of the cooling system by 40 percent.
But when you look at the DeepMind blog post (https://deepmind.com/blog/deepmind-ai-reduces-google-data-ce...), it looks like the 40% model is comparing to a baseline of doing nothing. So the question is : is this really something you require an AI research powerhouse like DeepMind for, or is it something a regular data science team could do?
Re: DeepMind in “very early stage” talks with National Grid to reduce UK energy use
#6I doubt this will use deep learning though. DL works best at detecting patterns in large quantities of data, whereas the time series data here is limited to just a few years...and the system has changed over those year, and continues to change significantly. I'd guess that 10% is the cost of ensuring supply, i.e. spinning up generators as contingency - any attempt to shave saving from that may increase risk of power…
I know a couple people working on using deep learning for financial timeseries. It can be beneficial even with relatively limited data (a couple of years with high granularity).
I also think the 10% figure is quite ambitious; especially given that they are at an early stage of negotiations.
Re: DeepMind in “very early stage” talks with National Grid to reduce UK energy use
#7The DeepMind datacenter project was very interesting, but a lot of the ML people I spoke to were quite dubious about how much of it was genuinely down to new AI/neural networks, and how much of it was Google PR to justify how much they spent on DeepMind. > DeepMind trained a neural network to more accurately predict future cooling requirements, in turn reducing the power usage of the cooling system by 40 percent. But…
>> Google PR to justify how much they spent on DeepMind - why would google try justifying for Deep mind and not to other tens-to-hundreds of companies it acquires in a year ?
Re: DeepMind in “very early stage” talks with National Grid to reduce UK energy use
#8I don't know much about electric grid engineering - are there opportunities for a ML approach to increase efficiency in ways other than the sort of better supply forecasting implied by this article.
The article does mention the losses involved in long distance transmission, but surely traditional approaches can already yield fairly well optimised planning for improving this sort of efficiency?
(Finally, this article seems quite light on details to me & doesn't mention a source Google press release or anything like that with more specifics. Maybe just better supply forecasting could yield bigger benefits that I imagine...)
Re: DeepMind in “very early stage” talks with National Grid to reduce UK energy use
#9The DeepMind datacenter project was very interesting, but a lot of the ML people I spoke to were quite dubious about how much of it was genuinely down to new AI/neural networks, and how much of it was Google PR to justify how much they spent on DeepMind. > DeepMind trained a neural network to more accurately predict future cooling requirements, in turn reducing the power usage of the cooling system by 40 percent. But…
I was going to say, I feel like this could be done with excel and some historical data combined with weather forecasting.
that being said, even if ML gets you a 0.5% advantage over spreadsheet math, that is a non-trivial amount of savings on a national scale.
Re: DeepMind in “very early stage” talks with National Grid to reduce UK energy use
#10I like these guys who are already running a "virtual power station" which can smooth out the peaks by switching off non-vital power loads:
With Machine Learning, natch: http://www.openenergi.com/virtual-power-station-with-big-dat...