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Title
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CNN-LSTM architecture for predictive indoor temperature modeling
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Author
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Abstract
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| 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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Language
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English
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Source (journal)
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Building and environment. - Oxford
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Publication
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Oxford
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2021
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ISSN
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0360-1323
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DOI
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10.1016/J.BUILDENV.2021.108327
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Volume/pages
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206
(2021)
, 9 p.
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Article Reference
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108327
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ISI
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000700077500004
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Medium
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E-only publicatie
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Full text (Publisher's DOI)
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Full text (open access)
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Full text (publisher's version - intranet only)
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