Model Predictive Control

Algeno currently manages the heating systems in over 40,000 apartments in Sweden. Our model-based control system helps property owners reduce their properties’ carbon footprint through smarter optimization of heating systems.

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What is Model Predictive Control and how does it work?

Modellbaserad styrning (MPC, eng. Model Predictive Control) är en avancerad metod för att optimera värmesystem i fastigheter. Den bygger på en dynamisk modell som kontinuerligt lär sig och anpassar sig efter fastighetens specifika egenskaper och beteende. Denna modell skapas och uppdateras automatiskt genom att analysera data från fastigheten och dess system, vilket eliminerar behovet av manuell inmatning av byggnadsinformation.

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Modellen syftar till att återspegla hur fastigheten reagerar på olika faktorer, både interna (t.ex. uppvärmning, ventilation) och externa (t.ex. utomhustemperatur, solinstrålning, vind). När modellen är tillräckligt tränad fungerar den som en digital tvilling av fastigheten, vilket möjliggör noggranna simuleringar och prediktioner.

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I nästa steg används modellen för att styra värmesystemet på ett optimalt sätt, baserat på förutbestämda mål och villkor. Fastighetsägaren definierar de mål som ska uppnås, exempelvis minimering av energianvändning eller effektuttag, samt de villkor som måste uppfyllas, såsom önskad inomhustemperatur.

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Styrsystemet tar även hänsyn till väderprognoser för de kommande 48 timmarna för att kunna agera proaktivt och anpassa uppvärmningen i förväg. Resultatet är en jämnare inomhustemperatur, minskad energi-förbrukning och lägre kostnader.

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What is the difference compared to traditional control using a temperature curve or room-feedback control?

Traditional heating system control is often based on a preset temperature curve that relates the outdoor temperature to the supply water temperature in the radiator system. Essentially, this means that a higher supply water temperature is used when outdoor temperatures are colder, and vice versa when outdoor temperatures are warmer. No information or feedback regarding the indoor climate is used in the control system; instead, the system relies on the temperature curve having been set correctly initially, with manual adjustments made in response to feedback from tenants or when a maintenance technician realizes that the indoor climate is not as expected.

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A more advanced form of traditional control is room-feedback control, which uses data from indoor sensors to adjust the supply water temperature. Simply put, the supply air temperature is increased if the indoor temperature is too low compared to the target temperature, and vice versa when the indoor temperature is too high.

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Although this is a step in the right direction, it still has significant limitations compared to model-based control, where the two main features that are missing are:

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Forecast-based control

Feedback-based control is a purely reactive form of control that responds only to changes in the building. Model-based control is essentially a proactive control system that utilizes both historical data and future forecasts. Since a model of the building’s behavior and characteristics is available, it is possible to simulate how the building will behave in the coming hours or days based on weather forecasts and how the heating system is regulated. By using forecasts for various weather factors, one can, for example, account for a rapidly dropping outdoor temperature expected tomorrow or lower the supply water temperature now, knowing that solar radiation will increase significantly in a few hours.
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The result is a much more consistent indoor climate that is not as heavily influenced by changes in external factors.

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Economic regulation

Feedback control aims to maintain a specific average indoor temperature at all times, but the economic aspect of the control is never taken into account. In model-based control, the optimization process has a clear economic objective: to minimize heating costs while adhering to the specified conditions. This means that control is always aimed at minimizing costs, which allows both energy and power costs to be included in the optimization. It also makes it possible to optimize control based on spot prices for electricity in cases where heat pumps are used as the primary energy source.

The result is minimized heating costs while adhering to the specified conditions.

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How do AI models learn the behavior of buildings?

One of the most challenging aspects of Algeno’s system is learning the behaviors and characteristics of different properties. For each individual property, a unique AI model is created that continuously learns how the buildings respond to changes in both internal and external factors. The models are entirely data-driven, meaning they rely solely on data from the properties and their systems. No manual steps or building information are required; instead, the AI models learn directly from how the property actually behaves.

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It is also important to continuously update the models, as properties behave differently depending on the season, and changes are constantly occurring within the property and its behavior.

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How can I learn more?

For more information, please contact us at info@algeno.se, and we’ll be happy to explain in detail how our technology works.