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                <term xml:lang="en">Machine Learning</term>
                <term xml:lang="en">District Heating Network</term>
                <term xml:lang="en">Model Predictive Control</term>
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              <p>District heating networks are urban-scale heat energy supply systems. These networks offer a flexible mannerto supply heat energy by leveraging the thermal inertia of the network pipes and the presence of distributed heatsources. To exploit such flexibility and reach energy supply efficiency, optimal operational control strategies ofthe networks are essential. However, existing models developed for an optimal control strategy are hinderedby their computational costs, rendering them impractical for large networks. The computational burden stemsmostly from the iterative simulation of the networks required for each optimization resolution step, until optimalcontrol parameters are attained. Indeed, each simulation of the network has computational costs proportional tothe size of the networks. In this study, we propose a hybrid Model Predictive Control (MPC), a controlframework using a hybrid model to simulate the network and optimize the mass flow rates and supplytemperatures injected by the heat sources to fulfill substation energy demands with the lowest surplus anddeficits of delivered heat. The proposed approach substitutes clusters of consumer substations with artificialNeural Networks (NN) models, trained to replicate real-time consumption patterns and leaving waters’temperatures evolution of the replaced clusters. This hybrid approach reduces computational costs whilemaintaining prediction errors below 0.52 %. Results demonstrate that replacing one-third of the network leadsto a reduction of 9% of the computational time required by a physics-based MPC.</p>
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              <p>Les réseaux de chauffage urbain sont des systèmes d'approvisionnement en énergie thermique à l'échelle urbaine. Ces réseaux offrent une solution flexible pour fournir de l'énergie thermique en tirant parti de l'inertie thermique des canalisations et de la présence de sources de chaleur distribuées. Pour exploiter cette flexibilité et atteindre une efficacité énergétique optimale, il est essentiel de mettre en place des stratégies de contrôle opérationnel avec optimisation. Cependant, les modèles existants développés pour une stratégie de contrôle optimal sont limités par leurs coûts de calculs intensifs, ce qui les rend impraticables pour les grands réseaux. Ce coût de calcul provient principalement de la nécessité de faire des simulations répétitives des réseaux, indispensable à chaque étape de résolution de l'optimisation, jusqu'à l'obtention des paramètres de contrôle optimaux. En effet, chaque simulation du réseau entraîne des coûts de calculs non négligeable, proportionnels à la taille du réseau. Dans cette étude, nous proposons un modèle de contrôle prédictif (MPC) basé sur une approche hybride, un cadre de contrôle utilisant un modèle hybride pour simuler le réseau. L'objectif du contrôle est d'optimiser les débits massiques et les températures injectées par les producteurs de chaleur, afin de satisfaire les demandes énergétiques des sous-stations avec un minimum de surplus et de déficits de chaleur délivrée. L'approche hybride proposée substitue des groupes de sous-stations de consommateurs par des modèles de réseaux neuronaux artificiels (NN), entraînés pour reproduire les schémas de consommation en temps réel et l'évolution des températures de l'eau dans les groupes remplacés. Cette approche hybride réduit les coûts de calculs global tout en maintenant des erreurs de prédiction inférieures à 0,52 %. Les résultats montrent que le remplacement d'un tiers du réseau permet une réduction de 9 % du temps de calcul requis par un MPC basé sur un modèle de simulation purement physique.</p>
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