Modeling and validation of a novel load model considering uncertain thermal disturbance in the district heating system

Abstract

To accomplish precise heating, district heating systems need to understand the demand of heat users comprehensively. Most current heating load prediction models were built on energy center control, with only a few involving user-side control. Thermal disturbance factors differ depending on the type of heat user. This work established a novel heating load model (BHLP-UN model) for the user side in an existing district heating system. The uncertain thermal disturbances were identified and integrated into the base model (BHLP model) with hybrid mechanisms and deep learning methods. The model was calibrated and validated using actual operational data from different types of heat users during multiple heating seasons. The findings showed that the mean absolute percentage error of the BHLP-UN model was decreased by 53.512% −65.338% for different types of heat users compared with the BHLP model, which only considered the meteorological factors and the thermal inertia. Indoor temperature was set as an input variable to effectively analyze the time-varying property of user demand for temperature. When the indoor temperature was 22℃ / 18℃ (working /non-working), the annual load could decrease to 26.501% − 28.572%. The model achieves long-term load trends with short-term forecast accuracy.