건물에너지시스템 센서 네트워크에서의 백업 가상센서 성능 향상을 위한 시계열 데이터 다운스케일링 방법

Abstract

Building system sensor networks measure and collect various information in buildings. Virtual sensors provide enormous potentials for improving and supplementing physical sensor-based building sensor networks. Virtual sensors are developed upon data collected from the sensor network and used for observation, backup, and prediction of system variables. A virtual sensor is a model which learns mathematical relations among input data to output the required variable. In this regard, aggregating various heterologous data (data fusion) has been important to obtain high-performance virtual sensors containing well-learned inner structures. However, fusing different data into single time series can be a difficult task due to the different sensing periods (data resolution). In this paper, a novel relational variable-based data downscaling method is suggested to tackle the limitations on building system data fusion. The effectiveness of the proposed method was evaluated with real operational datasets. The suggested method improved the performance of the virtual sensor by 30.2%.