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 connected with project's performance: 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 the publication!
The full text of article you can grab here.
By meantime let us tou 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.