Analyzing electric utility data using machine learning
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Analyzing electric utility data using machine learning
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Re: Analyzing electric utility data using machine learning
#2I think "recreated a small portion of their core research" would be a fairer title. The current title makes it sound like he's actually built a competitor to the company, when in fact, all he's proved is that basic tech is often an almost trivial part of a company, compared with all of the housekeeping and other busywork required to actually provide a service to customers.
Re: Analyzing electric utility data using machine learning
#3It's a great effort here and I appreciate the post, but I think the title is a little misleading. There's a whole lot more to a company, let alone a $500MM company, than its tech/product. It's like saying you reverse engineered a kettle and now you're Breville. Consider team, brand, adoption, supporting infrastructure/marketing channels/processes/etc etc etc.
I apologise if this is nitpicky, but I think a more accurate description would be "I reverse-engineered a $500M AI company's algorithm in one week".
Re: Analyzing electric utility data using machine learning
#4I think "recreated a small portion of their core research" would be a fairer title. The current title makes it sound like he's actually built a competitor to the company, when in fact, all he's proved is that basic tech is often an almost trivial part of a company, compared with all of the housekeeping and other busywork required to actually provide a service to customers.
Beat me to it. Strongly agree.
Re: Analyzing electric utility data using machine learning
#5I think "recreated a small portion of their core research" would be a fairer title. The current title makes it sound like he's actually built a competitor to the company, when in fact, all he's proved is that basic tech is often an almost trivial part of a company, compared with all of the housekeeping and other busywork required to actually provide a service to customers.
Indeed. The work described in this post is something I'd expect out of an intern after one or two weeks.
Re: Analyzing electric utility data using machine learning
#6I think "recreated a small portion of their core research" would be a fairer title. The current title makes it sound like he's actually built a competitor to the company, when in fact, all he's proved is that basic tech is often an almost trivial part of a company, compared with all of the housekeeping and other busywork required to actually provide a service to customers.
harsh reality but I agree
Re: Analyzing electric utility data using machine learning
#7I think "recreated a small portion of their core research" would be a fairer title. The current title makes it sound like he's actually built a competitor to the company, when in fact, all he's proved is that basic tech is often an almost trivial part of a company, compared with all of the housekeeping and other busywork required to actually provide a service to customers.
I have to add there is zero reverse-engineering here. The main purpose of this article seems to be name-dropping for the 500M company while trying to generate some 'thoughtleader'-cred
Re: Analyzing electric utility data using machine learning
#8I think "recreated a small portion of their core research" would be a fairer title. The current title makes it sound like he's actually built a competitor to the company, when in fact, all he's proved is that basic tech is often an almost trivial part of a company, compared with all of the housekeeping and other busywork required to actually provide a service to customers.
Indeed. The work described in this post is something I'd expect out of an intern after one or two weeks.
i.e. "I ran K-Means, here's the result."
Re: Analyzing electric utility data using machine learning
#9I think "recreated a small portion of their core research" would be a fairer title. The current title makes it sound like he's actually built a competitor to the company, when in fact, all he's proved is that basic tech is often an almost trivial part of a company, compared with all of the housekeeping and other busywork required to actually provide a service to customers.
When you're peddling knowledge, knowing the formula is a big deal.
Most utilities have existing load reduction programs in place that do this kind of analysis via subcontractors who build energy efficiently plans. In my state, you can get substantial energy discounts if you agree to let the system operator control a portion of your electric supply and shut stuff off when there is a peak usage event.
All of these contractors bid on this stuff. If I can pay this guy or some other random consultant guy to do this analysis with more modern techniques, I can get rid of 3-5 energy engineers @150k of payroll and benefits each. That means I win the bid.
I can guarantee you that paying this guy money that will make him do backflips will cost maybe 10% of what it would cost if I went to Oracle or IBM.
You folks who work with this stuff don't understand how poor the tooling is in verticals like this. If you ever have an opportunity to see IBM Watson people in the room with CIOs, take it and try not to shake your head too much. They demonstrate analysis that is much less robust than this of things like help desk tickets, and CIOs think they've met HAL.
Re: Analyzing electric utility data using machine learning
#10I really don't like the arrogance coming through the writing style, especially given how clear it is the author has at best basic working knowledge of ML.
Never mind the fact that k-means is ML 101 and the $500M company is likely using more sophisticated ones, the fact that he says the following tells me he's just reading tutorials and plugging data into libraries (which is fine but not with this tone of know-it-all writing):
"I played with the number of clusters, and the one that allowed me to get the most significant clusters was 6 (this was a trial and error approach, for brevity I’ll report just the final outcome)."
Anyone who has studied clustering knows you would at the very least do a scree plot here. You can defer to intuition but there's more to it than running kmeans and claiming you've reverse-engineered a $500M company.