3 ms·

I 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 jus

by redcalx 10y ago

I 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.

halflings 10y ago
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.
- londons_explore 10y ago
  It might be reducing transmission losses by 10%. Ie. trying to prefer supply near users to reduce losses in long transmission cables. The whole thing seems rather tricky though, because the entire grid right now is run on a market based approach, where suppliers and users bid for the right to sell/use power every half hour. The ML in that case would have to be given to every company to make smarter bids.
  - lostboys67 10y ago
    And reducing the use of standby gensets which is very expensive not to mention polluting
- redcalx 10y ago
  Yeh I'd guess electronic exchange financial time series have a lot of interesting dynamics at very fine detail because of high frequency trading, so it's a very different system than a power grid in that respect. And for the record I regard HFT as mostly being a wasteful activity that effectively amounts to a tax on using electronic exchanges - ok there's a fuzzy line between arbitrage (useful) and full on HFT (which is clearly pointless IMO).
  - kixiQu 10y ago
    I'm involved in very-unsexy not-Google-level smart grid research using machine learning. A Phasor Measurement Unit (PMU) like those being deployed in America takes sixty measurements a second for each signal, and this is in no way overkill for the granularity of information required.
StargazyPi 10y ago
Depends; the input data set could be massive, incorporating things like TV listings data (half-time kettle surges are a huge part of our power consumption profile), weather, publicly listed events, perhaps scraped from websites and so quite dirty. I can see this transitioning quite easily from a 'we can sanitise this data ourselves' job to a 'screw it, let a massive neural net figure this out' one.
- redcalx 10y ago
  My initial best guess is that the style of analysis you're suggesting (TV guides, etc), doesn't offer sufficient safety margin in the event that a prediction is wrong. If reducing the safety margin was an option then there are probably lots of things that can be done, but if the goal is to maintain a safety margin akin to the current one then my instinct is that the gains to be had are minimal - and that the interesting work is in power storage (battery tech, cryogenic storage, etc.)

by redcalx 10y ago

halflings 10y ago

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.

londons_explore 10y ago
It might be reducing transmission losses by 10%. Ie. trying to prefer supply near users to reduce losses in long transmission cables. The whole thing seems rather tricky though, because the entire grid right now is run on a market based approach, where suppliers and users bid for the right to sell/use power every half hour. The ML in that case would have to be given to every company to make smarter bids.
- lostboys67 10y ago
  And reducing the use of standby gensets which is very expensive not to mention polluting
redcalx 10y ago
Yeh I'd guess electronic exchange financial time series have a lot of interesting dynamics at very fine detail because of high frequency trading, so it's a very different system than a power grid in that respect. And for the record I regard HFT as mostly being a wasteful activity that effectively amounts to a tax on using electronic exchanges - ok there's a fuzzy line between arbitrage (useful) and full on HFT (which is clearly pointless IMO).
- kixiQu 10y ago
  I'm involved in very-unsexy not-Google-level smart grid research using machine learning. A Phasor Measurement Unit (PMU) like those being deployed in America takes sixty measurements a second for each signal, and this is in no way overkill for the granularity of information required.

StargazyPi 10y ago

Depends; the input data set could be massive, incorporating things like TV listings data (half-time kettle surges are a huge part of our power consumption profile), weather, publicly listed events, perhaps scraped from websites and so quite dirty. I can see this transitioning quite easily from a 'we can sanitise this data ourselves' job to a 'screw it, let a massive neural net figure this out' one.

redcalx 10y ago
My initial best guess is that the style of analysis you're suggesting (TV guides, etc), doesn't offer sufficient safety margin in the event that a prediction is wrong. If reducing the safety margin was an option then there are probably lots of things that can be done, but if the goal is to maintain a safety margin akin to the current one then my instinct is that the gains to be had are minimal - and that the interesting work is in power storage (battery tech, cryogenic storage, etc.)