2022. 10. 1. 19:00ㆍ인공지능,딥러닝,머신러닝 기초
LSTM-based indoor air temperature prediction framework for HVAC systems in smart buildings
Abstract
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
- control / uncontrol / 기상조건, 재실자 스케쥴 등 여러 요인들에의해 영향 받기 때문에 IAT 예측은 매우 어려움
This paper presents a long shortterm memory (LSTM) model to predict IAT for multi-zone building based on direct multi-step prediction with sequenceto-sequence approach
- 이 논문은 LSTM을 사용하여 멀티 존(StS접근 방시긍로 멀티 스텝 예측을 위한)IAT 예측을 함
Two strategies, LSTM-MISO and LSTM-MIMO, are built for multi-input single-output and multiinput 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
- LSTM-MISO(multi-inpu single-output) , LSTM-MIMO(multiinput-multioutput) 방법을 사용함
- 모델 성능은 VAV, CAV 통해 판단
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