Then I realised couple of things, an humbling experience:
1) given any position on earth, you can compute exactly what's the optimal inclination at any given point in time for a PV to maximize the energy production. Sure, there are reflection and secondary irradiation conditions (eg.: there is a lake close to it), but again, assuming the environment is static, it's way faster to just compute it statically rather than dynamically. Also, in most scenarios Beam irradiance from diffusion (the beam hitting the object) is order of magnitude higher than from reflective one (the same beam bouncing on a 3rd object first).
2) In mechanics movable part are the things to avoid. They have lower MTBF (mean time before failure) and as such they introduce complexity and increase cost
3) Economics is a key component of engineering. There is a cost to everything, the computational power, the energy needed by the servo, etc, etc. Given 1 and 2, a dynamic solution simply has a lower ROI than a static one.
I really appreciate the OP exploration here: there is a good overview of basic control theory and a good foundation of ML (although don't be deceived, this is a very simple modelling task that OP is overkilling with a way more complex model). That said, for everyone reading, this is not something you want to do in a real world situation.
Commodity fixed angle ground mount photovoltaics arrays are low cost.
If you do the dollar and kWh produced in year calculation for spending $40,000 on fixed mount ground pv, and $40k on a combination of pv panels on trackers, and compare the kWh proxied by both... The fixed ground mount comes out far ahead.
A tracking mount can make sense only if you have a VERY small amount of space to work with and want the absolute most kWh per month per square meter of area occupied on the ground. And don't care about money much.
But I do wonder if heliostats might see quite a revival in agrovoltaics: there, you want a certain distance between panels anyways, and perhaps the plants won't mind if you steal a little more light off-noon in exchange for less shadowing at noon. Electricity supply/demand would certainly applaud this bias, in a market with lots of photovoltaics a Wh at noon is certainly worth less than those closer the the periphery of the daily sun cycle.
And if you do agrovoltaics right, the structure will be expensive anyways (making the markup for heliostat insignificant) because imho it's still just an unfinished prototype if the structures for holding the panels aren't designed to double as an overhead rail system for farming powertools that could become a considerable efficiency gain over the century-old game of tractor vs mud.
(I spent quite a lot of time on an idea for rooftop solar thermal power and was trying to build a prototype when the solar panel prices started crashing. It pretty soon became inescapable fact that small scale solar thermal with all its moving parts just wasn't viable any more. I'd be surprised if even the large mirror-farm CSP is competitive these days.)
I'm no engineer, so I can't determine whether it would work, but on the surface it looks like it should?
A 'joke' we had in engineering school: Anyone can design something to do X for $5, but it's an engineer's job to get the same results for $3.
In this solar project, the metric should be a comparison to the yield from pointing at the sun based on lat,long, and (earth) time.
Really, the analysis would have to include anticipated costs of installation and maintenance in comparison to a dumb array.
Perhaps the ingenious author could consider xy or xyz movement in an intermittently shadowed environment instead of 2-axis rotation. This might be be a better job for machine learning, or just a well-known control system problem.
Better to add more panels.
A better way to ensure more power output is to have a set of panels with a small battery back to automate cooling of the panels and cleaning of the panels.
https://www.youtube.com/watch?v=MiADday0mDA
https://en.wikipedia.org/wiki/Wax_motor
tl;dr - sun rises, temperature rises, wax material expands, motor actuates, solar panel tracks the sky as if it was a sun flower. Maybe the gloop is sentient or something.
And the next day your panels are staring in the wrong direction.
Here is how they do it. https://gml.noaa.gov/grad/solcalc/calcdetails.html
Isn't this exactly the problem that can be better solved using real power data instead of values expected from theory ?
The trickiest part might be getting an accurate time on whatever cheap controller your using.
Just need your location and the time (quarter/season, month, day, hour, minute) and you'll know where the sun is in relation to the location given.
Maybe I'm missing something, but i would use a simpler algorithm which doesn't need ML. On day 0, plug in the latitude and allow the system to traverse the range of angles, finding the optimal one at the time - ie: yielding maximum power. Let it run 3-5 times during the day, then fit those points to the theoretical path of the sun across the sky. Now your system is calibrated, without needing any other input. As the seasons change, the system will always know which angle to face for optimal power.
[1]Micro hydro:
https://en.m.wikipedia.org/wiki/Micro_hydro
[2]Micro hydro power with turgo generator:
I wonder how much more accurate your system is and whether the tradeoff is worth the added expense of a motor + the additional maintenance cost of moving parts.
I wonder why solar farms don't use active tracking, is that added maintenance + equipment cost just not worth it?
This is incorrect, especially for solar farms
Panels are indeed only about 1/3 of the cost. Additional components, labour, inverter, mounting - another 1/3.
The last third is indeed soft(ish) cost, but this includes profit (duh..), certification, survey, tax, fees, etc. This can be reduced, but it is not going to magically disappear ...
Another fun fact : newer mega farms in low-altitude deserts are considering no-axis no-mounting zero-tilt - just laying the panels on the ground ... It all comes down to cost vs yield
A nice project by the way. Did you ever compare the results to pre-calculated angles based on time/location/season?
In my personal case I have 12+9 classic chains modules, I need more than 2x physical space to transform them with a dual-axis tracking setup. That means it's cheaper just add some fixed panels eastward and westward to catch extra power earlier and later.
Also in those terms: lithium storage is very expensive BUT for self-consumption is still the cheaper option to have electricity for more time, just arriving to a meaningful production 1/1.5h earlier and later in the day does not help much given it's added cost.
In costs terms: these days it's even cheaper (in TCO terms) having hot water heated by p.v. than the more efficient thermal because that cost more, have more moving parts and regular maintenance that just making an a bit bigger p.v.
The real issue in all cases is that to have enough power to really pay back the investment "quickly" we need much non-shadowed southward space witch can be found somewhere but far from everywhere. A similar issue is for EVs: I like the idea of charging them "for free" from solar, BUT since I normally use a vehicle during the day or I use it only sometimes or I have two or more in a round-robin scheme. Also lithium storage lifetime is an issue, on scale the production capacity and recycling are issues. Until we solve them just produce some more Wh it's meaningless...
We use intelligent power management over the fiber network to tell certain loads when to turn on or off or to change their operating parameters based on power conditions.
I’ve been daydreaming about building a ml based forecaster that just gives the next few hours weather outlook based on pressure, temperature, humidity, and a wide Nigel image of the sky.
I know it is doable because I can do it myself, and probably without any intuition about the pressure. It would automatically calibrate the model wights by feedback from the actual events vs the forecast. This would be really useful for me at least, in managing battery usage and otherwise managing the various systems that store energy like air compressors and large mass refrigeration.
https://www.youtube.com/watch?v=qguTFa9tj3c
Dyson Sphere Program · Covering Half a Planet with Solar Panels
https://www.youtube.com/watch?v=MKxkWgknkco
Dyson Sphere Program - Solar Panels
https://www.youtube.com/watch?v=yO78pXYnjFA
Full day night cycle of solar panels | Dyson Sphere Program
Will changing the orientation of the panel really have some effect on these (besides the drift in axis position) ?
Control theory is better if you know what you’re doing. ML is technical debt for sure.
