基于人群密度估计的空调末端及新风量分级调控方法

负荷估计是空调系统优化控制的关键一环,人员移动的随机性和不确定性,使得建筑空间人员负荷难以准确估计,导致现有控制策略控制效果欠佳,系统响应不及时,滞后性大,造成能源浪费以及建筑内部环境热舒适性降低.针对该问题,提出一种基于人群密度估计的空调末端及新风量分级控制策略.首先,采集建筑空间图像信息,建立多列卷积神经网络人群密度估计模型,获取人员数量及动态分布,计算人员实时负荷;其次,引入人员负荷控制因子,提出空调分级调控策略,实现空调末端及新风供给.实验结果表明,方法能够更好地维持建筑内部热环境稳定,系统响应速度更快,具有较好的节能潜力.

Air conditioning terminal and new air volume classification control based on crowd density estimation

Load forecasting is a key part of system optimization control of air conditioning system, and the randomness and uncertainty of personnel movement make it difficult to accurately estimate the load of building space personnel, resulting in poor control effect of existing control strategies, the slow response of the system, the waste of energy and the reduction of thermal comfort of the building interior environment. To solve these problems, an air conditioning terminal and new air volume classification control strategy based on crowd density estimation is proposed in this paper. Firstly, the image information of building space was collected and a model of multi-column convolution neural network crowd density estimation was established to obtain the number and dynamic distribution of personnel and calculate the real-time load of building space personnel. Secondly, the personnel load control factor was introduced and the air conditioning grading control strategy was put forward to realize the air conditioning terminal and fresh air supply. The experimental results show that the method proposed in this paper can better maintain the internal environment stability of the building, at the same time, the system has faster response speed and greater energy saving potential.