Abstract: In modern architecture, it is necessary to control and optimize the quality of indoor environment to ensure the high comfort and low energy consumption. Indoor environmental quality that contains a lot of uncertainties and nonlinear factors is difficult to be described by the traditional linear system. This paper takes the intelligent building laboratory of Xi'an University of Architecture and Technology as the research object. Based on linear relationship between the physical parameters and control parameters of the indoor environmental quality, the control and energy consumption optimization modeling is established, using the bilinear model according to the data measured. On this basis, the method of model predictive control is constructed and optimized by the ant colony algorithm. Experimental results show the effectiveness of the proposed approach.

Key words: Indoor environmental quality, bilinear model, model predictive control, colony algorithm

摘要： 在现代建筑中，需对建筑室内环境品质进行有效的控制和优化来确保较高的舒适性和较低的能耗。室内环境品质包含了诸多不确定因素和非线性因素，传统的线性系统无法对其描述与控制。首先以西安建筑科技大学智能建筑实验室为研究对象，定义室内环境品质各物理参数和控制量的线性关系，建立基于双线性模型的室内环境品质控制与优化数学模型并进行验证分析。在此基础上，构建了基于模型预测的室内环境品质控制方法，并采用蚁群算法对其优化。实验结果显示，在相同室内外环境条件下，实验采集的室内环境品质参数与模型输出参数吻合度高，验证了模型的准确性；同时，在能耗较低的情况下，基于蚁群优化的模型预测控制能够快速稳定的跟踪室内温度和二氧化碳浓度设置点。

关键词: 室内环境品质, 双线性模型, 模型预测控制, 蚁群优化