Unsteady Heat Flux Measurement and Predictions Using Long Short-Term Memory Networks

Abstract

Energy consumption modeling has evolved along with building technology. Modeling techniques can be largely classified into white box, gray box, and black box. In this study, the thermal behavior characteristics of building components were identified through time-series data analysis using LSTM neural networks. Sensors were installed inside and outside the test room to measure physical quantities. As a result of calculating the overall heat transfer coefficient according to the international standard ISO 9869-1, the U value of the multi-window with antireflection coating was 1.84 W/(m2∙K). To understand the thermal behavior of multiple windows, we constructed a neural network using an LSTM architecture and used the measured data-set to predict and evaluate the heat flux through deep learning. From the measurement data, a wavelet transform was used to extract features and to find appropriate control time-step intervals. Performance was evaluated according to multistep measurement intervals using the error metric method. The multistep time interval for control monitoring is preferably no more than 240 s. In addition, multivariate analysis with several input variables was performed. In particular, the thermal behavior of building components can be analyzed through heat flux and temperature measurements in the transient state of physical properties of pre-installed building components, which were difficult to access with conventional steady-state measurement methods.

Keywords

• heat flux
• unsteady state
• neural network
• deep learning
• LSTM (long short term memory)
• building window