Urban sustainability

Innovative simulation tools

Real-life models

Modelling Optimization of Energy Efficiency in Buildings for Urban Sustainability

MOEEBIUS introduces a Holistic Energy Performance Optimization Framework that enhances current modelling approaches and delivers innovative simulation tools which deeply grasp and describe real-life building operation complexities in accurate simulation predictions that significantly reduce the “performance gap” and enhance multi-fold, continuous optimization of building energy performance as a means to further mitigate and reduce the identified “performance gap” in real-time or through retrofitting.


We are very happy to announce that another scientific publication focused on MOEEBIUS achievements is released and available in open access! The article entitled: "Simulation-Based Evaluation and Optimization of Control Strategies in Buildings" has been prepared and written by research team: Georgios D. Kontes, Georgios I. Giannakis, Victor Sanchez, Pablo de Agustin-Camacho, Ander Romero-Amorrortu, Natalia Panagiotidou, Dimitrios V. Rovas, Simone Steiger, Christopher Mutschler and Gunnar Gruen. We express our gratefulnes to the entire scientific team for their effort and congratulate on the publication!

Publication Energies


The full text of the article is available here

Below let us present the abstract of the article:

Over the last several years, a great amount of research work has been focused on the development of model predictive control techniques for the indoor climate control of buildings, but, despite the promising results, this technology is still not adopted by the industry. One of the main reasons for this is the increased cost associated with the development and calibration (or identification) of mathematical models of special structure used for predicting future states of the building. We propose a methodology to overcome this obstacle by replacing these hand-engineered mathematical models with a thermal simulation model of the building developed using detailed thermal simulation engines such as EnergyPlus. As designing better controllers requires interacting with the simulation model, a central part of our methodology is the control improvement (or optimisation) module, facilitating two simulation-based control improvement methodologies: one based in multi-criteria decision analysis methods and the other based on state-space identification of dynamical systems using Gaussian process models and reinforcement learning. We evaluate the proposed methodology in a set of simulation-based experiments using the thermal simulation model of a real building located in Portugal. Our results indicate that the proposed methodology could be a viable alternative to model predictive control-based supervisory control in buildings.



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Wednesday, July 17, 2019


EU  This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 680517.