Data-driven approaches for deriving a soft sensor in a district heating network

Abstract

To facilitate the ongoing transition to fourth-generation district heating systems, it is necessary to find resource-efficient methods to model the differential pressure in a district heating network accurately. This paper has developed, tested, and compared data-driven methods to create a soft sensor for a pressure transmitter in the district heating network. The sensor is used to control the district heating load and therefore, is the most critical spot of the sensor network. The data-based modelling approaches chosen were transfer functions and neural networks. The data set was collected from Hafslund Oslo Celsio’s historical database for January -March 2021, when the heating demand is highest. The best convolutional neural network and a first-order transfer function give acceptable results in estimating the pressure transmitter signal. Both models have the simplest architectures within their model type, suggesting that the need for complex models in either approach is redundant.