Data-driven predictive model for feedback control of supply temperature in buildings with radiator heating system

Abstract

For residential buildings with radiator heating system, the supply temperature is conventionally adjusted by weather compensator, which results in large fluctuations in the indoor air temperature and high energy consumption of heating. The solutions on current researches mainly focus on heat prediction of demand side, so that the supply temperature can be adjusted according to the predicted heat demand and achieve a heat balance. However, this requires a significant amount of historical data, which limits their applicability. And the lack of pre-simulation platform of the new strategies makes their practical application more difficult. In this paper, a digital twinning of secondary district heating system (DHS) is created, which considers both human behavior and radiator heat dissipation characteristics. A new supply temperature regulate strategy based on indoor temperature feedback is proposed. It contains an indoor temperature prediction model, which require a small amount of measured data and can keep the prediction error within ±0.6 °C. The control effects are analyzed by comparing the proposed regulation strategy with the weather compensator. The result shows that the new temperature regulate strategy is superior to the weather compensator in maintaining indoor temperature and can save energy by 5.4%.