Sánchez, Víctor F.Garrido Marijuan, Antonio2021-09Sánchez , V F & Garrido Marijuan , A 2021 , ' Integrated model concept for district energy management optimisation platforms ' , Applied Thermal Engineering , vol. 196 , 117233 , pp. 117233 . https://doi.org/10.1016/j.applthermaleng.2021.1172331359-4311researchoutputwizard: 11556/1162Publisher Copyright: © 2021 The AuthorsDistrict 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%.19614715enginfo:eu-repo/semantics/openAccessjournal article10.1016/j.applthermaleng.2021.117233District HeatingDistrict ModellingModel Predictive ControlCo-simulationModelicaSupervised Machine LearningDistrict HeatingDistrict ModellingModel Predictive ControlCo-simulationModelicaSupervised Machine LearningEnergy Engineering and Power TechnologyMechanical EngineeringFluid Flow and Transfer ProcessesIndustrial and Manufacturing EngineeringSDG 7 - Affordable and Clean EnergyProject IDinfo:eu-repo/grantAgreement/EC/H2020/680517/EU/Modelling Optimization of Energy Efficiency in Buildings for Urban Sustainability/MOEEBIUSinfo:eu-repo/grantAgreement/EC/H2020/680517/EU/Modelling Optimization of Energy Efficiency in Buildings for Urban Sustainability/MOEEBIUSFunding InfoThe research activities leading to the described developments and results, were funded by the European Uniońs Horizon 2020 MOEEBIUS project, under grant agreement No 680517. Authors would like to ex-press their gratitude to the operator of the Vozdovac district heating system (Beogradske elektrane) for the specifications used to develop and calibrate the models, and to Solintel M&P, SL for developing the initial versions of the EnergyPlus models (including only the geometrical and constructive definition of the buildings), in the framework of the MOEEBIUS project.The research activities leading to the described developments and results, were funded by the European Uniońs Horizon 2020 MOEEBIUS project, under grant agreement No 680517. Authors would like to ex-press their gratitude to the operator of the Vozdovac district heating system (Beogradske elektrane) for the specifications used to develop and calibrate the models, and to Solintel M&P, SL for developing the initial versions of the EnergyPlus models (including only the geometrical and constructive definition of the buildings), in the framework of the MOEEBIUS project.http://www.scopus.com/inward/record.url?scp=85108806295&partnerID=8YFLogxK