Sensor data correction in building automation systems using deep learning - Calimbahin, Caezarina Marie F.; Festin, Susan P.; Pedrasa, Jhoanna Rhodette I. (Quezon City College of Engineering, University of the Philippines Diliman, 2019)
Building automation systems (BAS) are a class of cyber-physical systems (CPS) that aim to improve the energy efficiency of HVAC and other equipment through intelligent control. Typically, sensor measurements are used to compute for the optimal control action equipment. One of the problems that could arise in CPS are data integrity attacks. These are the ones where adversaries manipulate data sent to the controllers. The purpose of an empirical sensor model is to estimate correct sensor value from its corrupted observation. The two main strategies are hardware and analytical redundancy. Hardware redundancy refers to the addition of components in the system, such as extra sensors performing the same measurement. A final read-out is obtained through a voting mechanism. While this method is able to out-vote erroneous data, it entails high implementation cost. Analytical redundancy, on the other hand, estimates sensor data by means of mathematical models, derived from physics-based process description or from data-driven methods. This paper proposes an analytical model based on deep learning framework, specifically denoising autoencoders (DAE) for sensor data collection. The DAE is a feed-forward neural network with two main characteristics: (1) dimensionality reduction forces the model to learn corrections among input data that would allow minimum reconstruction error at the output, and (2) denoising capability allows robustness to partial corruption at the input. We show that the model was able to mitigate data integrity attacks in BAS by comparing with a baseline where no mode is employed.
Thesis (Master of Science in Computer Science)--University of the Philippines Diliman
Keywords: Detectors; Energy consumption; Building Automation Systems (BAS).; Cyber-Physical Systems (CPS).; Heating,Ventilation and Air Conditioning (HVAC).