Browsing by Keyword "info:eu-repo/grantAgreement/EC/H2020/680517/EU/Modelling Optimization of Energy Efficiency in Buildings for Urban Sustainability/MOEEBIUS"
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Item Design of knowledge-based systems for automated deployment of building management services(2020-11) Schneider, Georg F.; Kontes, Georgios D.; Qiu, Haonan; Silva, Filipe J.; Bucur, Mircea; Malanik, Jakub; Schindler, Zdenek; Andriopolous, Panos; de Agustin-Camacho, Pablo; Romero-Amorrortu, Ander; Grün, Gunnar; Tecnalia Research & Innovation; LABORATORIO DE TRANSFORMACIÓN URBANA; EDIFICACIÓN DE ENERGÍA POSITIVADespite its high potential, the building's sector lags behind in reducing its energy demand. Tremendous savings can be achieved by deploying building management services during operation, however, the manual deployment of these services needs to be undertaken by experts and it is a tedious, time and cost consuming task. It requires detailed expert knowledge to match the diverse requirements of services with the present constellation of envelope, equipment and automation system in a target building. To enable the widespread deployment of these services, this knowledge-intensive task needs to be automated. Knowledge-based methods solve this task, however, their widespread adoption is hampered and solutions proposed in the past do not stick to basic principles of state of the art knowledge engineering methods. To fill this gap we present a novel methodological approach for the design of knowledge-based systems for the automated deployment of building management services. The approach covers the essential steps and best practices: (1) representation of terminological knowledge of a building and its systems based on well-established knowledge engineering methods; (2) representation and capturing of assertional knowledge on a real building portfolio based on open standards; and (3) use of the acquired knowledge for the automated deployment of building management services to increase the energy efficiency of buildings during operation. We validate the methodological approach by deploying it in a real-world large-scale European pilot on a diverse portfolio of buildings and a novel set of building management services. In addition, a novel ontology, which reuses and extends existing ontologies is presented.Item Integrated model concept for district energy management optimisation platforms(2021-09) Sánchez, Víctor F.; Garrido Marijuan, Antonio; Tecnalia Research & Innovation; EDIFICACIÓN DE ENERGÍA POSITIVADistrict heating systems play a key role in reducing the aggregated heating and domestic hot water production energy consumption of European building stock. However, the operational strategies of these systems present further optimisation potential, as most of them are still operated according to reactive control strategies. To fully exploit the optimisation potential of these systems, their operations should instead be based on model predictive control strategies implemented through dedicated district energy management platforms. This paper describes a multiscale and multidomain integrated district model concept conceived to serve as the basis of an energy prediction engine for the district energy management platform developed in the framework of the MOEEBIUS project. The integrated district model is produced by taking advantage of co-simulation techniques to couple building (EnergyPlus) and district heating system (Modelica) physics-based models, while exploiting the potential provided by the functional mock-up interface standard. The district demand side is modelled through the combined use of physical building models and data-driven models developed through supervised machine learning techniques. Additionally, district production-side infrastructure modelling is simplified through a new Modelica library designed to allow a subsystem-based district model composition, reducing the time required for model development. The integrated district model and new Modelica library are successfully tested in the Stepa Stepanovic subnetwork of the city of Belgrade, demonstrating their capacity for evaluating the energy savings potential available in existing district heating systems, with a reduction of up to 21% of the aggregated subnetwork energy input and peak load reduction of 24.6%.Item A new era in the energy performance of buildings(2017-11-01) de Agustin-Camacho, Pablo; Romero-Amorrortu, Ander; Krysinski, Dawid; Tecnalia Research & InnovationImproving energy efficiency in buildings is a major priority for the European Union, yet current modelling processes do not accurately reflect consumption. The MOEEBIUS framework will provide the basis for more accurate energy performance assessment, underpinning efforts to improve efficiency and opening up new commercial opportunities, as Dawid Krysiński explainsItem Plotting a path to reduce the energy performance gap(2019-04-08) de Agustin-Camacho, Pablo; Romero-Amorrortu, Ander; Kowalska, Agnieszka; Tecnalia Research & InnovationImproving energy efficiency performance in buildings is a major priority for the European Commission, with a target of achieving 20 percent energy savings by 2020. The EU promotes solutions which reduce energy consumption in the building sector to achieve this, an area which forms the primary research focus for the MOEEBIUS projectItem Simulation-Based Evaluation and Optimization of Control Strategies in Buildings(2018-12-01) Kontes, Georgios; Giannakis, Georgios; Sánchez, Víctor; de Agustin-Camacho, Pablo; Romero-Amorrortu, Ander; Panagiotidou, Natalia; Rovas, Dimitrios; Steiger, Simone; Mutschler, Christopher; Gruen, Gunnar; Tecnalia Research & Innovation; EDIFICACIÓN DE ENERGÍA POSITIVA; LABORATORIO DE TRANSFORMACIÓN URBANAOver 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.