基于负荷预测的多联机变蒸发温度节能控制

doi:10.19784/j.cnki.issn1672-0172.2022.99.046

摘要/Abstract

摘要： 多联机空调系统运行负荷变化范围大,现有定蒸发温度控制策略往往导致系统运行能效低,室内温度波动大等问题。开发了变蒸发温度控制策略,通过预测负荷,实时调节蒸发温度设定值,提高系统效率并改善室内温湿控制。控制策略的核心是空调负荷的快速在线预测,通过建立（Elman和长短期记忆神经网络,long short term memory,LSTM）两类数据模型,替代复杂且计算速度较慢的建筑物理模型。Elman和LSTM模型的平均相对误差分别为8.6%和2%,LSTM精度高,计算速度快,适宜与机组控制模块耦合。在此基础上,通过多联机系统模型和建筑模型的联合仿真,对基于负荷预测的变蒸发温度控制策略的运行效果进行了验证。研究表明,与定蒸发温度控制相比,变蒸发温度控制策略在兼顾房间热舒适性的前提下,其在广州典型制冷季的平均节能率为14%。该方法可应用到多联机的远程云端智能控制,降低建筑能耗及碳排放量。

关键词: 负荷预测, 多联机, 节能控制

Abstract: Due to the wide range of cooling load of variable refrigerant volume, VRV system, the existing constant setting point of evaporation temperature control strategy always causes issues such as low performance efficiency and unsteady room temperature and humidity, especially with low part load ratio. A new kind of evaporation temperature control strategy of VRV system has been developed to improve the energy efficiency of the system and indoor climate control by setting variable evaporation temperature with predicted load. Thus, the key of the control strategy is to predict load on-line rapidly. Two kinds of data-driven models (Elman and along short-term memory neural network models, LSTM) have been developed to replacing the physical model, which is too complicated and time-consuming for VRV control module. The mean relative deviations of Elman and LSTM models are 8.6% and 2.0%. And LSTM model has been selected to couple with the control module due to its rapid computation speed and high precise. Furthermore, the effect of the load-prediction based variable evaporating temperature control strategy has been verified by the co-simulation of a VRV system model and a building model. According the simulation results, the VRV system applied the load-based variable evaporation temperature control strategy could consume less power than that of constant evaporation temperature control strategy under the low-load condition. And about 14% energy saving could be achieved by the new control strategy during the cooling operation period in Guangzhou, while the requirement of indoor thermal comfort has been satisfied. The load-based control strategy could be applied in remote intelligent cloud control system for VRV to reduce building energy consumption and carbon emission.

Key words: Load prediction, Variable refrigerant volume system (VRV system), Energy-saving control

中图分类号:

TB65

王婷, 吴信宇, 李亚平, 欧汝浩, 刘和成. 基于负荷预测的多联机变蒸发温度节能控制[J]. 家电科技, 2022, 0(zk): 212-217.

WANG Ting, WU Xinyu, LI Yaping, OU Ruhao, LIU Hecheng. Load-prediction based variable evaporating temperature control strategy of variable refrigerant volume system[J]. Journal of Appliance Science & Technology, 2022, 0(zk): 212-217.

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