Cheng Chin-Chi, Lee Dasheng
Department of Energy and Refrigerating Air-Conditioning Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.
Sensors (Basel). 2019 Aug 6;19(15):3440. doi: 10.3390/s19153440.
The study continues the theoretical derivation from Part 1, and the experiment is carried out at a bus station equipped with six water-cooled chillers. Between 2012 and 2017, historical data collected from temperature and humidity sensors, as well as the energy consumption data, were used to build artificial intelligence (AI) assisted heating ventilation and air conditioning (HVAC) control models. The AI control system, in conjunction with a specifically designed prior information notice (PIN) sensor, was used to improve the prediction accuracy. This data collected between 2012 and 2016 was used for AI training and PIN sensor testing. During the hottest week of 2017 in Taiwan, the PIN sensor was used to conduct temperature and humidity data predictions. A model-based predictive control was developed to obtain air conditioning energy consumption data. The comparative results between the predictive and actual data showed that the temperature and humidity prediction accuracies were between 95.5 and 96.6%, respectively. Additionally, energy savings amounting to 39.8% were achieved compared to the theoretical estimates of 44.6%, a difference of less than 5%. These results show that the experimental model supports the theoretical estimations. In the future, a PIN sensor will be installed in a chiller to further verify the energy savings of the AI assisted HVAC control.
该研究延续了第一部分的理论推导，并在一个配备了六台水冷式冷水机组的公交站进行了实验。在2012年至2017年期间，利用从温度和湿度传感器收集的历史数据以及能源消耗数据，建立了人工智能（AI）辅助的供暖通风与空调（HVAC）控制模型。AI控制系统与专门设计的先验信息通知（PIN）传感器相结合，用于提高预测准确性。2012年至2016年期间收集的数据用于AI训练和PIN传感器测试。在2017年台湾最热的一周，PIN传感器用于进行温度和湿度数据预测。开发了基于模型的预测控制以获取空调能耗数据。预测数据与实际数据的对比结果表明，温度和湿度预测准确率分别在95.5%至96.6%之间。此外，与理论估计的44.6%相比，实现了39.8%的节能，差异小于5%。这些结果表明实验模型支持理论估计。未来，将在冷水机组中安装PIN传感器，以进一步验证AI辅助HVAC控制的节能效果。
