Phyvac: a Python module for highly flexible HVAC system simulation, and fault dataset generation as an application example

Shohei Miyata, Shanrui Shi, Yasuhori Akashi, Takao Sawachi

Proceedings of Building Simulation 2023: 18th Conference of IBPSA, pp. 916-923, September 2023

https://doi.org/10.26868/25222708.2023.1370

Abstract

Accurately predicting indoor temperature distribution can provide valuable reference data, helping residents independently adjust HVAC equipment around them to ensure comfort while reducing unnecessary energy consumption. This study proposes a prediction framework composed of two neural networks, enabling accurate indoor temperature distribution prediction with minimal training data in both temporal and spatial dimensions. The Dual-Stage Attention-Based Recurrent Neural Network calculates the importance ranking of feature values to enhance individual feature information and reduce training data volume, while Long Short-Term Memory is used to predict time-series features. From February to September 2022, 22 temperature sensors were installed in a target office to collect minute-by-minute indoor temperature data, which served as training and testing datasets. The results showed that, for short-term prediction, using data from five out of the 22 sensors collected over two weeks in winter (heating season) and summer (cooling season), the framework accurately predicted temperatures at the remaining 17 sensor locations, with a root mean squared error between 0.3 and 0.7. This study is significant for continuous indoor temperature prediction under low data volume conditions.