That’s for a single one but at tens of MW even a bunch of satellites isn’t going to get solar panels to produce an appreciable amount of power.
This video goes into the details of what kind of performance we can expect from the constellation
That’s for a single one but at tens of MW even a bunch of satellites isn’t going to get solar panels to produce an appreciable amount of power.
This video goes into the details of what kind of performance we can expect from the constellation
No the observed power on the ground is on the order of mW/m².
Comparable to moonlight and so far no one has classified that as a weapon.
As always with these revolutionary startups, be careful with what you believe:
EEVblog 1637: Solar Freakin’ Space Mirrors! - Reflect Orbital DEBUNKED
At least this one is actually possible and doesn’t attempt to defy the laws of physics.
Also crashes for me with 0.2.1
Found this comment with some links. Couldn’t find anything from an admin during my short search.
The exact same problem arose for Voyager users in March when Voyager dropped support for Lemmy 0.18.
For some people logging out and back in has helped but I’ve seen multiple beehaw users state that this doesn’t work for them.
This seems to be because beehaw is intentionally staying on an old Lemmy version.
Not sure how the Dev wants to handle this since they’ve got enough work on their hands and this issue should resolve itself once beehaw upgrades.
For now your best bet is to try re-logging and if that doesn’t work to roll back to a previous version of Eternity.
This person had the same issue and they’ve just logged out and in again
Always mocking Dr. Daniel Jackson. Poor guy
Additional information regarding Home Assistant:
The sun component (which should be enabled by default) already computes the sun position for you.
Elevation and azimuth are available as standalone sensors sensor.sun_solar_azimuth
(might be disabled by default) or as attributes on the sun.sun
entity.
I don’t have any experience with it but this might do something along those lines(?):
https://esphome.io/components/binary_sensor/ble_presence.html
Seems like you can just add it to one or more of your existing esphome devices.
Cushy is an experimental Graphical User Interface (GUI) crate for the Rust programming language. It features a reactive data model and aims to enable easily creating responsive, efficient user interfaces. To enable easy cross-platform development, Cushy uses its own collection of consistently-styled Widgets.
I could only find the Model 3 in their statistic.
The best value for 2021 is 0.8 by the Audi A4 and A5, whilst the worst is the Toyota RAV4 with 17.6.
Overall they rank the Model 3 with “very low” and “low” rate of failure.
Granted these cars are still pretty young so who knows what that figure will look like in 5 or 10 years.
For context they seem to be specifically referencing the 12V “starter” battery not the HV battery used for the traction drive in EVs with that 44.1% figure. Additionally this figure seems to include all vehicles in the statistic, so some part of that is contributed by ICE vehicles.
If you have such a system up and running already you could try to modify it before ripping it out and starting from scratch.
Borrowing an idea from the machine learning approach you could additionally take the difference in average outside temperature yesterday and the average forecasted outside temperature today. Then multiply that by a weight (the machine learning approach would find this value for you but a single weight can also be found by hand) and subtract it from the target temperature before the division step discussed previously. Effectively saying “you don’t need to heat as much today since it will be a little warmer”.
I fear that’s about all you can do with this approach without massively overcomplicating things.
This is effectively what a thermostat does.
The problem is that the controller won’t know how well insulated each room is, how cold it is outside (including wind speed), which doors and windows are open and when, what people or devices are doing in each room.
The way thermostats solve this is by creating a closed loop where they react to how the room reacts to their actions.
Depending on how your heaters work you’ll likely need some dynamic component to react to these unforeseen changes unless you can live with the temperature being very unstable.
To get a rough idea of how long the heaters will have to run you can look at each room in for the last n days and see if the heater’s runtime was long enough to (on average) hold your target temperature. Dividing the average temperature with the target temperature will give you an idea whether they were on for too long or too short. (If the heaters have thermostats you’ll likely need to subtract a small amount from that value so that it will settle at the minimum required heating time)
If that value is close to 1.0 you know that on those days the heating time was just about perfect.
Once that is the case you can take the previous days heating time and divide it up over the cheapest hours. The smaller of a value n you choose the more reactive the system will be but it will also get a little more unstable. Depending on your house and climate this system described here might simply be unsuitable for you because it takes too long to react to changes.
There are many other ways to approach this very interesting problem. You could for example try to create a more accurate model incorporating weather and other data with machine learning. That way it could even do rudimentary forecasting.
Are there any implementations of this out there or is this purely theoretical (at this point in time)?
Adding a Turing award to your profile is certainly one way to flesh it out
Why not set up backups for the Proxmox VM and be done with it?
Also makes it easy to add offsite backups via the Proxmox Backup Server in the future.