[HN Gopher] Launch HN: InBalance (YC W21) - Short-term energy ma...
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Launch HN: InBalance (YC W21) - Short-term energy market
forecasting
Hey everyone, we're Thomas and Raj, cofounders of inBalance
(inbalanceresearch.com). inBalance forecasts electricity price,
demand, and generation by source up to 72 hours ahead, helping
utilities and independent power producers utilize their responsive
assets such as energy storage, backup generators, flexible demand,
etc more efficiently. We met playing ultimate frisbee in
Cambridge, UK, and quickly found common interests in statistics and
optimization. Thomas had previously worked on wind turbine
placement problems, providing experience with power markets, and we
discussed them but didn't see an immediate entry point, so Thomas
continued his statistics PhD and Rajan worked in ML research and
GPU algorithm design at a startup. A year ago we heard of a need
for better wind power forecasts and started to look at the market
more closely. We found a gap emerging from the increase in the
prevalence of storage, especially lithium-ion, grid-scale
batteries. It seemed like an interesting and useful real-world
application of machine learning, particularly with the possibility
of reducing carbon emissions, so once the business case looked
tenable, we decided to go ahead! Electrical power markets have
become increasingly volatile due extreme weather events and
increased prevalence of intermittent renewables. In response to
this, producers are bringing on more flexible generation assets
such as batteries to even out fluctuations in supply, and
electrical consumers are aiming to increase their ability to
modulate demand to better take advantage of cheap intermittent
power. These assets don't fit into the day-ahead markets designed
for mostly traditional steam power plants, making it difficult to
choose when to use them. Our forecasts help traders better align
their use with power availability, who now do so on gut feeling or
low-quality coarse-grained forecasts. We hope this will increase
the value enough to make transitioning to renewables more
financially appealing. Most standard machine learning approaches
struggle in particular with price forecasting due to the limited
data, large number of factors, heavy-tails, high noise, and
underlying complexity; even given the bids for each producer and
consumer, solving for the prices across a power network taking into
account transmission, energy balance, and AC power flow constraints
relies on an NP-hard mixed-integer programming problem that can
take hours to solve. Of course in reality we don't even know the
bids ahead of time, and we still haven't won the battle against the
heavy tails today! Our pilot experiences with a major East Coast
utility looking to trade power, a major New England utility
managing their demand response program, a battery storage operator
in Texas, and a wind trader in Texas, have shown us that every
participant has differing needs for their particular asset
collection, so we dedicate time to each of our customers to make
sure that the product is tailored to their needs. Along the way
we've developed a generic forecasting system tuned for power
markets to speed up customization, but we know we have a long way
to go before we support the full range of forecast granularity,
location, range, risk metrics etc we've heard interest for. With
over 3000 market participants operating in open electricity markets
(including Texas, California, New York, New England, and the mid-
Atlantic), we're hoping to hit 7 figure revenue within the next
year. We need huge amounts of storage to facilitate a transition
to zero carbon grids long term, so we hope to minimize risk and
maximize the reward for building new storage assets. We'd love to
hear your thoughts, questions, and comments!
Author : Straw
Score : 40 points
Date : 2021-06-04 16:22 UTC (6 hours ago)
| hubrix wrote:
| Let's chat, I founded a profitable energy business. Find me in
| the YC investor list.
| santiagobasulto wrote:
| Awesome! This seems like a big and hard problem to tackle. I'm
| also in the ML space; my only recommendation (and I can't say
| anything in terms of energy because I'm a complete ignorant in
| the matter) is: try to contribute as much as possible to Open
| Source. Most AI-based profitable companies seem to find good
| market traction after leveraging Open Source esteem and renown.
|
| Best of luck!
|
| PS: I'm from Argentina and I've also made for-life friends
| playing Ultimate :)
| MrBull53 wrote:
| That's one of the dumbest things I've ever heard. How do open
| source contributions do anything?
| Straw wrote:
| Hi, thanks for the advice!
|
| How would you balance maintaining an open source presence with
| maintaining a technological advantage based on our ML
| techniques in the space? We've put a lot of effort into finding
| modelling approaches that work in the high-noise, heavy-tailed,
| limited data environment, so we feel we can't just give them
| away.
|
| P.S. Yeah, its the best! And for some reason it seems to
| attract all the nerds :)
| hubrix wrote:
| Let's chat. I founded a profitable energy business. You can find
| me in the YC investor list.
| jll29 wrote:
| Some energy companies I know have their own rooms full of
| analysts looking into things like what's on TV tonight so they
| can forecast demand spikes e.g. in the breaks of sports events.
| They also have data science teams to place their wind farms
| optimally in-house (my knowledge of the sector is geographically
| limited to Europe and the midwest of the US).
|
| So make sure that whatever you are getting into is a true need
| that more than one potential customer actually has, rather than
| basing things on assumptions alone (customer validation of the
| proposition).
| thomasmarge wrote:
| a number of utilities we work with, including several larger
| players, currently rely heavily on third party forecasts of
| demand, wind generation, etc
|
| but you're right, some folks we've approached have built large
| in-house teams
| BullMr35 wrote:
| Have you considered the long term effects of Musk's tweets on the
| markets? How would you use these to bias a model? (Congrats btw
| :))
| thomasmarge wrote:
| nlp is difficult, do you think his tweets could have a direct
| impact on short term power consumption patterns?
| BullMr35 wrote:
| NLP is difficult, yes, but modern transformer architectures
| are able to interpret even Elon's tweets. I think there's a
| great deal of potential for a direct impact, particularly
| with Bitcoin's high energy demand. I also wouldn't discount
| the potential for him to shift his focus onto energy markets
| and make more direct tweets. When Tesla power banks properly
| come on line, you'll need to be directly wired into his
| information flow as well. Basically, yes! Get on it :))
| monkeydust wrote:
| Congrats on launch.
|
| A quality problem to be solving.
|
| Would like to see a real world detailed walkthrough of how your
| product is solving a problem with one of those pilot customers
| and +1 to open source per previous comment.
| Straw wrote:
| Thanks!
|
| Perhaps we could show one of our forecasts, and implied
| battery/turbine/etc dispatch plan, but lagged a day to avoid
| annoying the customer of said forecasts? We could even prove we
| had them earlier by first publishing a hash ;).
| danielmarkbruce wrote:
| Maybe start a hedge fund and speculate on electricity prices?
| thomasmarge wrote:
| I think trading is an important application of our modeling
| work, at least it would help bring real time pricing into
| equilibrium with 36 hour ahead futures so that power plants
| with poor ramping constraints can plan accordingly
| _1tan wrote:
| Are you looking at markets outside the US as well?
| Straw wrote:
| Definitely. Our initial work is all in the US, but we're also
| keeping an eye on European power markets. Although they have a
| slightly different structure, its the same underlying problem.
|
| We haven't looked outside the US and Europe, anywhere you'd
| recommend?
| thomasmarge wrote:
| Canadian markets are also very interesting to us, we've heard
| Ontario (IESO) utilities are currently underserved in several
| pilots
| an_opabinia wrote:
| > differing needs for their particular asset collection
|
| How do you deal with customers who, on the one hand, generate
| most of their revenue from the honest work of doing A for person
| B, while on the other hand, generate _profits_ from completely
| esoteric financial and political engineering?
| Straw wrote:
| What kind of customer are you referring to? How would our
| forecasts benefit them?
| an_opabinia wrote:
| > What kind of customer are you referring to?
|
| The kind of small energy companies that answer the phone when
| a startup calls.
|
| Presumably if you had an idea for how to do the forecasts
| profitably you'd just become an energy futures trader
| yourself.
|
| I don't know, I'm not trying to poo-poo your thing. But the
| ESG / Biden green energy plan / renewables stuff your people
| are excited about, you make a lot more profit just by showing
| up and harvesting the incentives than you do like, doing a
| good job.
|
| Berkshire did this (https://www.bloomberg.com/opinion/article
| s/2019-06-04/warren...) and it's funny, those people were a
| scam. That's what I'm trying to say. Profits are pretty dodgy
| in the energy industry.
|
| I like this teeny tiny example though, Active Power, that
| became a Net Operating Loss shell (also common for biotech),
| because energy has an easier time booking losses as R&D
| expenses
| (https://seekingalpha.com/article/4126904-p10-industries-
| purc...).
|
| I don't know. There are so, so many examples of hilarious bad
| actors, from which the profits for small energy businesses
| come. You're not making forecasts for _Exxon_ , you're doing
| it for little tiny energy providers. How, really, are they
| profitable at all?
| Straw wrote:
| You raise a good point! Actually, we do plan to trade day-
| ahead energy futures ourselves. However, in several US
| markets, the markets required for responsive assets such as
| batteries and gas turbines simply don't exist- there isn't
| an hour-ahead electricity future. So there's value outside
| what we could capture by trading directly.
|
| Now, if we could get enough interest to make such a short-
| term futures market and trade on it directly... we'd love
| to!
| herewego wrote:
| I think you may be conflating energy usage optimization
| with futures trading. Utilities often use forecasts for
| different purposes than futures traders, e.g.
| demand/response. And there are many utilities that aren't
| aren't tiny. In fact, most are larger than they seem (it's
| expensive to be in this business), and all of them are
| looking to optimize heavily. I work in this field in a
| similar capacity to these guys and there is money to be
| made legitimately.
| [deleted]
| idiotsecant wrote:
| There are many midsize utilities that need accurate load
| forecasting to fulfill their duties as Balancing
| Authorities and ensure that generation equals load. These
| are not small companies and the methods currently used are
| often improvable.
| MrBull53 wrote:
| You honestly have the consider the esoteric intraoperative
| activity that deflate and conflate profitability along the
| journey to the end.
| ashbrahma wrote:
| Great initiative. Congrats on the launch. Fluence recently bought
| AMS (Advanced Microgrid) to provide similar services. Others like
| Invenia Labs are also trying to tackle this problem. Would love
| to connect directly with you to speak. Email is on my profile.
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(page generated 2021-06-04 23:00 UTC)