4 ms·
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 h
by 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 agoIt 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 agoAnd reducing the use of standby gensets which is very expensive not to mention polluting
- redcalx 10y agoYeh 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 agoI'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.