LSTM-based indoor air temperature prediction framework for HVAC systems in smart buildings - Mtibaa, Fatma; Nguyen, Kim-Khoa; Azam, Muhammad; Papachristou, Anastasios; Venne, Jean-Simon; Cheriet, Mohamed (Neural Computing and Applications, 2020)
Accurate indoor air temperature (IAT) predictions for heating, ventilation, and air conditioning (HVAC) systems are challenging, especially for multi-zone building and for different HVAC system types. Moreover, the nonlinearity of the buildings thermal dynamics makes the IAT prediction more difficult since it is affected by complex factors such as controlled and uncontrolled points, outside weather conditions and occupancy schedule. This paper presents a long short-term memory (LSTM) model to predict IAT for multi-zone building based on direct multi-step prediction with sequence-to-sequence approach. Two strategies, LSTM-MISO and LSTM-MIMO, are built for multi-input single-output and multi-input multi-output, respectively. The performance of these two strategies has been evaluated based on two case studies on real smart buildings using variable air volume (VAV) and constant air volume (CAV) systems. For both buildings, experimental results showed that the LSTM models outperform multilayer perceptron models by reducing the prediction error by 50%.
KerkoCite.ItemAlsoKnownAs: 10.1007/s00521-020-04926-3 5072953:SHT4N4QB 5072953:WQTUHTQI openalex: W3021449555
Keywords: CAV; HVAC; LSTM; Multi-step ahead predictions; Sequence-to-sequence; VAV; _important; _z:class:countries; _z:class:themes; oa:topic:T10121 Building Energy Efficiency and Thermal Comfort Optimization; oa:topic:T10766 Urban Heat Islands and Mitigation Strategies; oa:topic:T11371 Urban Wind Environment and Air Quality Modeling; openalex:n:1; openalex:yes; selected_for:Predictive models for classroom conditions 2024