Over two decades, a research group has been established at the Offenburg University of Applied Sciences in the environment of Prof. Elmar Bollin, which brought together the two areas of building automation and sustainable energy technology.
Energieeffizienz im Gebäude: Algorithmus ermöglicht Prognose von Energiebedarf.
Initially, the goal was to exploit potentials of internet-based weather forecasting and model-based system control for improving comfort and energy efficiency in buildings . Research and development work using dynamic building simulations eventually yielded an algorithm, called AMLR, that made it possible to predict an office building's energy demand for the following day based on predicted outdoor temperature and solar radiation. In conjunction with building automation, the predictive TASBS control AMLR was thus created.
Thermoactive building systems (TABS) are characterized by the fact that, due to the enormous thermal mass, large time delays occur in their regulation, especially during transition periods. This can lead to considerable losses in comfort combined with energy expenditures for their compensation. This is where predictive control finds an ideal field of application. The research group around Prof. Elmar Bollin was now able to prove after years of development work, starting with computer simulation over research experiments with climate chambers up to the application in real numerous office buildings, that the control and regulation of TABS with the prognosis-based tool AMLR as part of the building automation not only saves energy, but also the comfort in TABS heated and cooled office rooms is considerably improved.
For the application of AMLR no additional devices are needed. Only the building automation system must have the ability to collect weather forecast data from the Internet and process it accordingly. What is also special about the AMLR program from Offenburg is that co-author Dr. Martin Schmelas succeeded in making the parameterization of the predictive control algorithm self-learning, i.e. adaptive, as part of his doctorate at Offenburg University of Applied Sciences, which significantly reduces the effort required to commission the predictive-controlled TABS.
In the technical book "TABS - Thermoactive Component Systems: Self-learning and Predictive Control with AMLR" by Elmar Bollin and Martin Schmelas, which has just been published by Springer-Vieweg, the AMLR algorithm is explained in detail. In addition, all development steps of AMLR, from the first computer simulation to the application in office buildings, are documented. Thus, it is possible for building designers and building automation engineers to use the predictive control of TABS in all climates. For more information about the technical book, see .
MLR-Modell zur Vorhersage des Energiebedarfs eines Büroraumes
Vereinfachtes Widerstands-Kapazitätsmodell für die Beladung von TABS
