【摘要】 准确的房间冷负荷预测是空调运行过程节能的基础.首先,根据房间能量平衡方程,通过分析供冷量、冷负荷和蓄热量的关系,提出调温模式下房间负荷预测模型;然后,利用频域分解法实现蓄热计算,应用深度循环神经网络实现温度恒定条件下冷负荷预测;最后,综合温度变化下的蓄热量和温度恒定条件下的冷负荷预测,得到调温模式下房间冷负荷预测值.为提升深度学习算法收敛速度,在深度循环神经网络反向传播修正参数的过程中引入了高斯-牛顿法-LM (Levenberg-Marquardt)法自适应切换的学习算法.仿真实验和实测实验均表明,该方法能快速有效地实现房间逐时负荷预测.本方法实现了调温模式下房间负荷需求的快速精确计算,可用于实现建筑被动热储能的定量计算,同时为整个电网需求侧直接负荷控制提供可借鉴的思路.更多还原

【Abstract】 Accurate prediction value of room cooling load is the basis data for energy conservation in air conditioning operating process. Firstly, according to the room energy balance equation, with analysis of relationship among cooling load, heat extraction and heat storage, the room cooling load prediction model is put forward. The frequency domain decomposition method is employed to realize the calculation of heat storage, while the deep recurrent neural network is employed to realize the prediction of room cooling load under room air constant condition. Finally, combining the heat storage under room air temperature fluctuation condition and room cooling load under room air constant condition,the room cooling load prediction value under temperature control mode can be obtained.In order to improve the learning efficiency of deep recurrent neural networks, an adaptive switching method between Gauss-Newton method and Levenberg-Marquardt(LM) method is introduced. The building energy consumption simulation toolkits and real-test based experiments show that the proposed method can realize the prediction of hourly room cooling load quickly and efficiently. The proposed model and method realizes accurate prediction for room cooling load under temperature control mode, and can be utilized to realize the quantitative analysis for building passive thermal energy storage, as well as provide references for the direct load control in power grid demand side management.更多还原