Multi-zone temperature prediction in a commercial building using artificial neural network model

Hao Huang, Lei Chen, Morteza Mohammadzaheri, Eric Hu, Minlei Chen

نتاج البحث: Conference contribution

34 اقتباسات (Scopus)

ملخص

Predicting temperature in buildings equiped with Heating, ventilation and air-conditioning (HVAC) systems is a crucial step to take when implementing a model predictive control (MPC). This prediction is also challenging because the buildings themselves are nonlinear, have many uncertainties and strongly coupled. Artificial neural networks (ANNs) have been used in previous studies to solve such a modeling problem. Unlike most of the studies that have only considered small-scale, single zone modeling task, this paper presents a novel ANN modeling method for the modeling inside a real world multi-zone building. By comparing ANN models with different input variables, it was found that the prediction accuracies can be greatly improved when the thermal interactions were considered. The proposed models were used to perform both single-zone and multi-zone temperature prediction and achieved very good accuracies.

اللغة الأصليةEnglish
عنوان منشور المضيف2013 10th IEEE International Conference on Control and Automation, ICCA 2013
الصفحات1896-1901
عدد الصفحات6
المعرِّفات الرقمية للأشياء
حالة النشرPublished - 2013
منشور خارجيًانعم
الحدث2013 10th IEEE International Conference on Control and Automation, ICCA 2013 - Hangzhou, China
المدة: يونيو ١٢ ٢٠١٣يونيو ١٤ ٢٠١٣

سلسلة المنشورات

الاسمIEEE International Conference on Control and Automation, ICCA
رقم المعيار الدولي للدوريات (المطبوع)1948-3449
رقم المعيار الدولي للدوريات (الإلكتروني)1948-3457

Other

Other2013 10th IEEE International Conference on Control and Automation, ICCA 2013
الدولة/الإقليمChina
المدينةHangzhou
المدة٦/١٢/١٣٦/١٤/١٣

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