3 research outputs found
Physics-informed Neural Network Modelling and Predictive Control of District Heating Systems
This paper addresses the data-based modelling and optimal control of District
Heating Systems (DHSs). Physical models of such large-scale networked systems
are governed by complex nonlinear equations that require a large amount of
parameters, leading to potential computational issues in optimizing their
operation. A novel methodology is hence proposed, exploiting operational data
and available physical knowledge to attain accurate and computationally
efficient DHSs dynamic models. The proposed idea consists in leveraging
multiple Recurrent Neural Networks (RNNs) and in embedding the physical
topology of the DHS network in their interconnections. With respect to standard
RNN approaches, the resulting modelling methodology, denoted as
Physics-Informed RNN (PI-RNN), enables to achieve faster training procedures
and higher modelling accuracy, even when reduced-dimension models are
exploited. The developed PI-RNN modelling technique paves the way for the
design of a Nonlinear Model Predictive Control (NMPC) regulation strategy,
enabling, with limited computational time, to minimize production costs, to
increase system efficiency and to respect operative constraints over the whole
DHS network. The proposed methods are tested in simulation on a DHS benchmark
referenced in the literature, showing promising results from the modelling and
control perspective