| Popis: |
District heating system (DHS) is an important part of the urban energy system. But its large scale and numerous coupling variables bring many difficulties to its regulation and control. At present, rough regulation carried out by manual experience is hard to guarantee the quality of regulation, thus caused problems about heating comfort and efficiency. This paper proposes a data-driven temperature response prediction model to predict secondary loop supply temperature (SLST) considering the historical operating status of heating substation, valve opening, and weather conditions, etc. Non-time sequence models (MLP, XGBoost) and time sequence model (LSTM) prediction models established under different input and prediction lengths respectively are proposed and tested in this article. Results show that the prediction performance of the XGBoost model of 72 input steps and 12 prediction steps is best, with a root mean square error of 0.114°C. It reaches high prediction accuracy and can be used to guide the SLST control and primary loop valve opening (PLVO) regulation of heating substations. |
