CNN-LSTM architecture for predictive indoor temperature modeling

Abstract

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.