基于XGBoost的供热系统热力站调控预测方法

XGBoost-based regulation and prediction method for the heating station′s heating system

热力站精准预测调控为降低供热系统供热能耗、提升用户热舒适性和降低污染物排放提供关键数据支撑。为了充分利用热力站历史运行状态、阀门未来设定开度、天气特征等关键数据对热力站二次供、回水温度进行精准调控,提出了一种基于极限梯度提升(XGBoost)的供热系统热力站调控预测方法。对上述数据进行预处理、特征构造、模型训练和预测生成热力站温度响应预测模型,结合二次网温度延迟时间进而确定阀门调控策略。提出的方法在24 h的预测温度和实际温度对比中,最大预测误差为0.47℃,验证了热力站温度响应模型的准确度和可靠性,可用于指导热力站的温度调控。

Accurate prediction and control of heating stations provide solid data support for the heating systems′energy consumption reduction,users′thermal comfort improvement and pollutant emission alleviation.Making full use of a heating station′s key data,such as historical operating states,the set openings of valves and the weather characteristics,a heating station control and prediction method based on Extreme Gradient Boost(XGBoost)is proposed to make accurate regulation on secondary heating supply and return water temperature.Preprocessing,feature construction and model training are conducted on the data above,then a heating station temperature response prediction model is generated.Combining the model and temperature delay time of the secondary network,the valve control strategy is thereafter made.The error between the 24 h predicted temperature and the measured temperature is kept within 0.47℃,which verifies the accuracy and reliability of the heating station temperature response prediction model.The proposed strategy can guide the temperature control of heating stations.