I’ve been working as an automation technician for around 5 years now, and I’ve come across several services that promise a substantial improvement in energy efficiency when it comes to heating, especially involving district heating with water. Most of these are subscription services.

While they usually quote numbers that are based in reality, the way they are accomplished is often covered in technical jargon and straight up BS that a person without experience won’t be able to understand and so believes the seller.

That’s where this post comes in.

All that the impressive sounding, weather pattern considering, ultra-futuristic solution really does is take the existing setpoint for the heating system and lowers it by ≈2°C. That’s it. One line of code. Takes any competent professional 5 minutes to implement. What’s worse, some systems are laid on top of the old one, with a higher priority. Meaning that you need a specialist provided by the “optimization” company to adjust the heating. So if you’re too cold or too hot, tough shit. Your building’s maintenance company can’t change it.

The existing system in most cases already bases its setpoints on the current outside temperature, with a pollrate of around a second. What the weather is going to be like next week, or even tomorrow, does not matter. The system already adjusts the setpoints in real time.

Maybe a bit of a niche rant, but hopefully helps at least someone. Thanks for reading!

  • itstoowet@lemmy.world
    18·
    9 months ago

    Yeah… most optimizations for District Heating are simple (not actually so simple, there are a lot of variables to consider and the networks are very complex) calculations to figure out how much you can lower forward temperatures while maintaining customer requirements.

    But there are some cool things going on that actually look to optimize these massive heating networks. For instance, my company creates digital twins (1:1 emulations that model the network in real time) that optimize the energy usage by leveraging analytics, ML, and physics modeling, to lower energy usage.

    This is starting to trickle down to the consumer level as well, I’m already talking to engineers doing this for individual buildings.

    Disclaimer: not an engineer

    Thanks for the niche rant in an industry I actually work in!

    • EarWorm@lemmy.worldOP
      6·
      9 months ago

      Thanks for commenting, I didn’t expect to find someone on “the other side” of the argument! Just to be clear, can you explain what all these models and calculations accomplish that can’t be done with temperature sensors in a few of the apartments, for example? That’s the solution that the more advanced systems I’ve implemented use, and it at least sounds significantly simpler. And no need for a subscription.