期刊
BUILDING AND ENVIRONMENT
卷 206, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.buildenv.2021.108327
关键词
HVAC; Indoor temperature modeling; CNN-LSTM; LSTM; MLP; Closed-loop prediction; Black-box modeling
The study introduces a model combining Convolutional Neural Networks with Long Short Term Memory for indoor temperature modeling, showing excellent performance in long-term predictions and better robustness against errors. Experimental results demonstrate the model's ability to accurately predict room temperature within a 120-minute prediction horizon.
Indoor temperature modeling is a crucial part towards efficient Heating, Ventilation and Air Conditioning (HVAC) systems. Data-driven black-box approaches have been an attractive way to develop such models due to their unique feature of not requiring detailed knowledge about the target zone. However, the noisy and non-linear nature of the problem remains a bottleneck especially for long prediction horizons. In this paper, we introduce a Convolutional Neural Networks-Long Short Term Memory (CNN-LSTM) architecture to combine the exceptional feature extraction of convolutional layers with the Long Short Term Memory (LSTM)'s capability of learning sequential dependencies. We experimentally collected a dataset and compared three approaches: Multi-Layer Perceptron (MLP), LSTM and CNN-LSTM. Models are evaluated and compared with 1-30-60-120 min horizons with a closed-loop prediction scheme. The CNN-LSTM outperformed all other models for all prediction horizons and showed a better robustness against error accumulation. It managed to predict room temperature with R-2 >0.9 in a 120-min prediction horizon.
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