Data Compensation with Gaussian Processes Regression: Application in Smart Building’s Sensor Network

Abstract

Data play an essential role in the optimal control of smart buildings’ operation, especially in building energy-management for the target of nearly zero buildings. The building monitoring system is in charge of collecting and managing building data. However, device imperfections and failures of the monitoring system are likely to produce low-quality data, such as data loss and inconsistent data, which then seriously affect the control quality of the buildings. This paper proposes a new approach based on Gaussian process regression for data-quality monitoring and sensor network data compensation in smart buildings. The proposed method is proven to effectively detect and compensate for low-quality data thanks to the application of data analysis to the energy management monitoring system of a building model in Viet Nam. The research results provide a good opportunity to improve the efficiency of building energy-management systems and support the development of low-cost smart buildings.

Design of Mathematical Model and Selected Coefficient Specifications for Composite Materials Reinforced with Fabric from Waste Tyres

“…It can handle non-linear relationships between input and output variables and captures uncertainty in the forecast. Gaussian regression is also considered a powerful and adaptive machine-learning technique that combines different machine-learning tasks [ 19 ]. This enables its use for a wide range of applications.…”