建筑暖通空调冷水温度节能控制方法设计

Design of Energy Saving Control Method for Cold Water Temperature of Building HVAC

由于目前已有方法未能对建筑暖通空调水系统的运行工况进行辨识,导致控制结果不理想,冷水温度调节时间较长,水泵运行能耗上升。提出一种超低能耗建筑暖通空调冷水温度优化控制方法,将人工神经网络和遗传算法相结合,对建筑暖通空调水系统的运行工况进行辨识。通过辨识结果,建立建筑暖通空调冷水温度优化控制器,利用RBF神经网络对控制器进行在线优化,获取房间预测温度输出。利用预测信息和设定的目标函数,不断进行在线修正预测温度输出,得到最佳暖通空调冷水温度,实现空调冷水温度优化控制。仿真结果表明,所提方法能够有效降低冷水温度调节时间和水泵运行能耗,获取满意的优化控制结果。

As the existing methods fail to identify the operating conditions of the building HVAC water system,the control results are not ideal,the cold water temperature regulation time is long,and the energy consumption of pump operation increases.An optimal control method for cold water temperature of HVAC in ultra-low energy consumption buildings is proposed,which combined artificial neural network with genetic algorithm to identify the operating conditions of the HVAC water system in buildings.Based on the identification results,the optimal controller of HVAC cold water temperature was designed.And then RBF neural network was used to optimize the controller on line,thus obtaining the predicted temperature output of the room.Moreover,the predictive Information and the given objective function were adopted to continuously correct the predicted temperature output on line and thus to obtain the best temperature of HVAC cold water.Finally,the optimal control was achieved.Simulation results show that the proposed method can effectively reduce the adjustment time of cooling water temperature and the energy consumption of pump operation,thus achieving a satisfactory outcome in optimization control.