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Preprints, Working Papers, ... Year : 2024

Leveraging Neural Networks in a Hybrid Model Predictive control framework for district heating networks

Abstract

District heating networks are urban-scale heat energy supply systems. These networks offer a flexible manner to supply heat energy by leveraging the thermal inertia of the network pipes and the presence of distributed heat sources. To exploit such flexibility and reach energy supply efficiency, optimal operational control strategies of the networks are essential. However, existing models developed for an optimal control strategy are hindered by their computational costs, rendering them impractical for large networks. The computational burden stems mostly from the iterative simulation of the networks required for each optimization resolution step, until optimal control parameters are attained. Indeed, each simulation of the network has computational costs proportional to the size of the networks. In this study, we propose a hybrid Model Predictive Control (MPC), a control framework using a hybrid model to simulate the network and optimize the mass flow rates and supply temperatures injected by the heat sources to fulfill substation energy demands with the lowest surplus and deficits of delivered heat. The proposed approach substitutes clusters of consumer substations with artificial Neural 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 while maintaining prediction errors below 0.52 %. Results demonstrate that replacing one-third of the network leads to a reduction of 9% of the computational time required by a physics-based MPC.
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Dates and versions

hal-04611500 , version 1 (13-06-2024)

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  • HAL Id : hal-04611500 , version 1

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Dubon Rodrigue, Mohamed Tahar Mabrouk, Bastien Pasdeloup, Patrick Meyer, Bruno Lacarrière. Leveraging Neural Networks in a Hybrid Model Predictive control framework for district heating networks. 2024. ⟨hal-04611500⟩
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