Ojas Pradhan

Drexel University | DU · Department of Civil, Architectural and Environmental Engineering

This paper documents the development and validation of a dynamic primary cooling and thermal storage system simulation testbed. The system simulation testbed, sIBAL, is based on the Intelligent Building Agents Laboratory (IBAL) at the National Institute of Standards and Technology (NIST), which is a research infrastructure and testbed for the devel...

Buildings consume about 40% of primary energy in the U.S., and 51% of the primary energy usage in commercial buildings are consumed by heating, ventilation and air conditioning (HVAC) system. Malfunctioning sensors, components, and control systems, as well as degrading HVAC and lighting components are main the reasons for energy waste and unsatisfa...

The increasing use of remote or mobile access, integrated wearable technologies, data exchange, and cloud-based data analytics in modern smart buildings is steering the building industry towards open communication technologies. The increased connectivity and accessibility could lead to more cyber-attacks in smart buildings. On the other hand, physi...

In recent years, there has been a growing trend towards the development of smart buildings that rely on cyber-physical systems (CPS) to optimize occupant comfort, safety, and energy efficiency. To ensure the reliable and efficient operation of CPS with designed control strategies, it is important to evaluate their performance under various scenario...

With the wide adoption of building automation system, and the advancement of data, sensing, and machine learning techniques, data-driven fault detection and diagnostics (FDD) for building heating, ventilation, and air conditioning systems has gained increasing attention. In this paper, data-driven FDD is defined as those that are built or trained f...

Fault detection and diagnosis (FDD) technologies are critical to ensure satisfactory building performance, such as reducing energy wastes and negative impacts on occupant comfort and productivity. Existing FDD technologies mainly focus on component-level FDD solutions, which could lead to mis-diagnosis of cross-level faults in heating, ventilating,...

Sampling is a technique to help identify a representative data subset that captures the characteristics of the whole dataset. Most existing sampling algorithms require distribution assumptions of the multivariate data, which may not be available beforehand. This study proposes a new metric called Eigen-Entropy (EE), which is based on information en...

Increasing advancements in building digitization, smart sensing, and metering technologies have allowed large amount of data to be collected and saved for monitoring, analyzing, and controlling building systems. However, due to sensors or communications failure, the data collected are often incomplete and poor in quality. Data imputation approaches...

Building automatic fault detection and diagnosis (AFDD) technologies have shown great potential for energy savings. To enable AFDD, a baseline depicting the normal operation mode is needed to detect whether the building operation deviates from normality. Existing research using physics-based knowledge and models for AFDD has mainly taken a trial-an...

Literature on building Automatic Fault Detection and Diagnosis (AFDD) mainly focuses on simulated system data due to high expenses and difficulties of obtaining and analyzing real building data. There is a lack of validation on performances and scalabilities of data-driven AFDD approaches using simulated data and how it compares to that from real b...

Studies indicate that a large energy saving can be realized by applying automatic fault detection and diagnosis (AFDD) to building systems, which consumes more than 40% of the primary energy in the U.S. To enable AFDD, a baseline depicting the normal operation mode is needed to detect whether the building operation deviates from normality. Differen...

A comparative study between using a dynamic Bayesian network (DBN) against using a static Bayesian network (BN) for building heating ventilating, and air conditioning fault diagnosis (HVAC) is presented. Contrarily to a static BN, DBN method incorporates temporal dependencies between fault nodes between timesteps using temporal conditional probabil...