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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.
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.
Und an dem Punkt könnte man die Routen und Fahrzeiten dieser Fahrzeugverbände zentral steuern, damit sie immer grüne Welle haben.
Dieses Werk könnte man zB ein Stell-Center nennen.
The 44.1% battery failure figure is regarding the “starter” battery (12V) and is combined from all vehicles in the study (EV and ICE).
The HV Battery for the traction drive is grouped together with any kind of motor failure and comes in at 22.8 %. But this figure also includes ICE vehicles ejecting piston rods etc.
The only EV vs ICE numbers stated directly are the total failures per 1000 vehicles at 1.9 (EV) and 3.6 (ICE).
I’d be really interested in a chart showing the failure categories separated by EV and ICE.
Dr. med. Maurice Cabanis (einer der Experten) ist schon ein bisschen sus
Out of curiosity I’ve let it rate Low<-Tech Magazine, a website run on an ARM SBC powered exclusively with off-grid solar power, and that only achieves 87% / A.
Can’t exactly remember which car it was but some of the early and smaller EVs didn’t necessarily come with a navigation system. Think along the lines of Chevy Bolt or Nissan Leaf.
Not OSM or Open Source but “A Better Route Planner” (ABRP) was one of the first good EV routing apps and got pretty popular.
Especially early on it was often smarter than the built-in routing systems if the car even had one.
Also available as a website: ABRP
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.
You can use their online web-editor (similar to OverLeaf for LaTeX) or download the open-source engine and run it locally (there are extensions available for many text editors).
Compared to LaTeX I find it much more comfortable to work with. It comes with sane, modern defaults and doesn’t need any plugins just to generate a (localized) bibliography or include links.
Since Typst is very young compared to LaTeX I’m sure that there are numerous docs / workflows that can’t be reproduced at the moment but if you don’t need some special feature I’d recommend giving it a shot.
Not a monetary one, no.
* (there might exist some business power tariffs that coincidentally benefit from this but nothing you’d use at home)
I started out with WireGuard. As you said its a little finicky to get the config to work but after that it was great.
As long as it was just my devices this was fine and simple but as soon as you expand this service to family members or friends (including not-so-technical people) it gets too annoying to manually deal with the configs.
And that’s where Tailscale / Headscale comes in to save the day because now your workload as the admin is reduced to pointing their apps to the right server and having them enter their username and password.
One sensor should be enough. I believe they usually mount onto the inside of the window facing outwards so that lights and movement in the room don’t influence it.
The simplest way of solving this would be with technically four separate automations. However you can place them all in the same HA Automation using multiple triggers and trigger IDs. (Or have one for the blinds and one for the lights with two triggers each)
I’m going to assume the blinds are somewhat light translucent.
For the blinds use a numeric trigger that fires if the lux value is over some threshold for let’s say 10 minutes. That way it won’t trigger for every tiny cloud. When triggered lower the blinds.
Add another numeric trigger for moving back up when the lux value is under some threshold for 10 mins. Test to make sure that lowering or raising the blinds doesn’t darken or lighten the room enough to immediately have it trigger the other trigger. If it does then increase the difference between the two thresholds.
Copy the same procedure for the lights. The timer can be shorter here, maybe try 1 minute. Make sure that the thresholds are low enough as otherwise lowering the blinds would immediately turn the lights on. I would suggest first tuning the blind triggers and then tuning the light thresholds to your liking.
If you can’t set the light thresholds low enough so that the blinds don’t interfere with them you’ll need a somewhat smarter automation but I’d try the easy way first.
Getting the configs to work with my personal devices was already a little finicky but doing that for not-so-technical family members was starting to be a bit too much work for me.
I’m hoping that Headscale will cut that down to pointing their app at the server and having them enter their username and password.
Was running Wireguard and am now in the process of changing over to Tailscale (Headscale).
It uses Wireguard for the actual connections but manages all the wireguard configs for you.
As in video wallpapers? Sure. KDE Plasma for one lets you install a bunch of wallpaper plugins ranging from video playback to live computed shaders and everything in between.
HAOS does the same thing where it runs HA Core (the actual server software) in docker.
There are two possibly major drawbacks to managing docker yourself:
Proxmox on the other hand manages full virtual machines for you. There is an official HAOS virtual machine image which gives you the benefit of doing everything for you while still allowing you to run other virtual machines alongside it. Proxmox can manage full VM backups for you and collects performance statistics for each VM. Main drawback would be the added complexity and increased resource usage.
Cool to see support for the QOI format in a popular software package.
The “add to home screen” button turns into an “install” button when Firefox detects that the website is a progressive web app (PWA). Other browsers do the same.
The difference is that a PWA can define a custom icon and name for the “app” button on your home screen and that it can use some clever caching making many PWAs offline capable (meaning you don’t need an internet connection to open the web page).
I understand the reluctance to press “install” but in the case of PWAs the install size is tiny and fully contained in Firefox and you get the added benefit of faster startup / loading times due to caching.
This person had the same issue and they’ve just logged out and in again