Indoor environment data time-series reconstruction using autoencoder neural networks

TL;DR

This paper presents a data-driven autoencoder neural network approach to accurately reconstruct missing short-term indoor environment data in building monitoring, improving data quality for building operation models.

Contribution

The study introduces three autoencoder neural network models specifically designed for reconstructing missing indoor environment data in building datasets, outperforming traditional numerical methods.

Findings

Autoencoder models outperform classic numerical approaches.

Achieved average RMSEs of 0.42°C, 1.30%, and 78.41 ppm for temperature, humidity, and CO2.

Models are applicable to various indoor environment data streams.

Abstract

As the number of installed meters in buildings increases, there is a growing number of data time-series that could be used to develop data-driven models to support and optimize building operation. However, building data sets are often characterized by errors and missing values, which are considered, by the recent research, among the main limiting factors on the performance of the proposed models. Motivated by the need to address the problem of missing data in building operation, this work presents a data-driven approach to fill these gaps. In this study, three different autoencoder neural networks are trained to reconstruct missing short-term indoor environment data time-series in a data set collected in an office building in Aachen, Germany. This consisted of a four year-long monitoring campaign in and between the years 2014 and 2017, of 84 different rooms. The models are applicable for different time-series obtained from room automation, such as indoor air temperature, relative humidity and $CO_{2}$ data streams. The results prove that the proposed methods outperform classic numerical approaches and they result in reconstructing the corresponding variables with average RMSEs of 0.42 {\deg}C, 1.30 % and 78.41 ppm, respectively.