Conference Paper

Proceedings of Building Simulation 2021: 17th Conference of IBPSA

A gap-filling method for room temperature data based on autoencoder neural networks

Antonio Liguori ¹, Romana Markovic ², Jérôme Frisch ¹, Andreas Wagner ², Francesco Causone ³, Christoph van Treeck ^1
1 E3D - Institute of Energy Efficiency and Sustainable Building, RWTH Aachen University, Mathieustr. 30, 52074 Aachen, Germany
² Building Science Group, Karlsruhe Institute of Technology, Englerstr. 7, 76131 Karlsruhe, Germany
³ Department of Energy, Politecnico di Milano, Via Lambruschini 4, 20156 Milano, Italy

DOI: https://doi.org/10.26868/25222708.2021.30232
Abstract: This study explores the applicability of a deep learning-based approach for reconstructing missing room temperature data from different domains where relatively few training samples are available. For that purpose, the existing convolutional, long short-term memory (LSTM) and feed-forward autoencoders were combined with a suitable domain adaptation procedure. Eventually, the developed models were evaluated on data collected in four buildings with significant differences in thermal mass, design and location. The findings pointed out that the domain adaptation can be conducted efficiently by using a small data sample from the target domain. Additionally, the results showed that the proposed model can reconstruct up to 80 % of the missing daily room temperature inputs with RMSE accuracy of 0.6 °C.
Keywords: neural networks, autoencoders, indoor environmental quality, missing data, occupant behavior
Pages: 2427 - 2434
Paper: