This study proposes a deep learning model utilizing the BACnet (Building Automation and Control Network) protocol for the real-time detection of mechanical faults and security vulnerabilities in building automation systems. Integrating various machine learning algorithms and outlier detection techniques, this model is capable of monitoring and learning anomaly patterns in real-time. The primary aim of this paper is to enhance the reliability and efficiency of buildings and industrial facilities, offering solutions applicable across diverse industries such as manufacturing, energy management, and smart grids. Our findings reveal that the developed algorithm detects mechanical faults and security vulnerabilities with an accuracy of 96%, indicating its potential to significantly improve the safety and efficiency of building automation systems. However, the full validation of the algorithm’s performance in various conditions and environments remains a challenge, and future research will explore methodologies to address these issues and further enhance performance. This research is expected to play a vital role in numerous fields, including productivity improvement, data security, and the prevention of human casualties.

View Video Presentation: https://doi.org/10.2514/6.2021-3363.vid Net positive water and energy buildings aim to generate or harvest more water and energy than they consume. Buildings pursuing this goal typically do so through the generation of energy using photovoltaic (PV) arrays and through the collection of rainwater using the building’s rooftop and a large cistern for storage. As PV systems are limited by their intermittency in power generation, energy storage systems (ESS) are being investigated to augment the availability of their generated power. Many ESS have been investigated, but little previous work has looked into the potential use of rainwater harvesting cisterns as passive thermal storage reservoirs for thermal-electric energy exchange. This work performed a simple investigatory study into this idea using actual data from an academic building located on Georgia Tech’s Atlanta campus. An energy and water building performance model was coupled with a cistern thermal energy storage model. The sensitivity of the building’s PV array area, cistern volume, maximum cistern temperature, and thermal-electric conversion efficiency are presented. The addition of a high specific heat material to the cistern is also evaluated and the impact of this addition is presented. Preliminary results suggest that an academic building may save up to 58% of its supplementary electrical demand by adopting a passive cistern thermal energy storage system.