Occupancy Estimation Using Low-Cost Wi-Fi Sniffers

Real-time measurements on the occupancy status of indoor and outdoor spaces can be exploited in many scenarios (HVAC and lighting system control, building energy optimization, allocation and reservation of spaces, etc.). Traditional systems for occupancy estimation rely on environmental sensors (CO2, temperature, humidity) or video cameras. In this paper, we depart from such traditional approaches and propose a novel occupancy estimation system which is based on the capture of Wi-Fi management packets from users' devices. The system, implemented on a low-cost ESP8266 microcontroller, leverages a supervised learning model to adapt to different spaces and transmits occupancy information through the MQTT protocol to a web-based dashboard. Experimental results demonstrate the validity of the proposed solution in four different indoor university spaces.Comment: Submitted to Balkancom 201

Analytical Content Vulnerability Assessment Methodology for Earthquake Catastrophe Models

The scarcity of detailed claims data for building contents (Coverage C) from historical earthquake events poses a significant challenge for property insurance catastrophe models to reliably estimate the losses associated to building contents. To develop content vulnerability functions empirically, one would need to have access to data from a multitude of historical events; however, loss disaggregation by coverage is rarely reported even when claims data become available from recent significant events such as Maule (2010) and Tohoku (2011). While damage to the building structure (Coverage A) can be estimated analytically using simulation-based fragility functions to amend sparse historical observations, the adoption of analytical approaches for other coverages is limited in the current generation of catastrophe models. In the absence of analytical methods, content loss estimation often relies on a combination of expert opinion and abstract reasoning on top of precious-little available data which is often limited to residential properties. In this paper, the authors employ FEMA P-58’s component-based methodology to develop a framework for simulation-based derivation of content vulnerability functions. Following a review of published literature and the types of content components in FEMA P-58’s PACT library, the authors present the simulation-driven vulnerability function for a four-story office building in Los Angeles, and compare the results against respective functions for office buildings from commercial models. Moreover, this paper discusses the need for new content component types in offices and professional service occupancy. Through this study, the authors demonstrate the possibility of improving content loss estimates in catastrophe models by adopting approaches similar to those involved in the development of structural vulnerability functions

Data-driven Demand Control Ventilation Using Machine Learning CO2 Occupancy Detection Method

Heating, ventilation, and air-conditioning (HVAC) system accounts for approximately 40% of total building energy consumption in the United States. Currently, most buildings still utilize constant air volume (CAV) systems with on/off control to meet the thermal loads. Such systems, without any consideration of occupancy, may ventilate a room excessively and result in a waste of energy. Previous studies show that CO2-based demand-controlled ventilation methods are the most widely used strategies to determine the optimal level of supply air volume. However, conventional CO2 mass balanced models do not yield an optimal estimation accuracy. In this manuscript, a data-driven control strategy was developed to optimize the energy consumption of supply fans by feed-forward neural network to predict real-time occupancy as an active constraint. As for the validation, the experiment was carried out in an auditorium located on a university campus. The result shows, after utilizing feed-forward neural network to enhance the occupancy estimation, the new primary fan schedule can reduce the daily ventilation energy by 75% when compared to the current on/off control

State-of-the-art on research and applications of machine learning in the building life cycle

Fueled by big data, powerful and affordable computing resources, and advanced algorithms, machine learning has been explored and applied to buildings research for the past decades and has demonstrated its potential to enhance building performance. This study systematically surveyed how machine learning has been applied at different stages of building life cycle. By conducting a literature search on the Web of Knowledge platform, we found 9579 papers in this field and selected 153 papers for an in-depth review. The number of published papers is increasing year by year, with a focus on building design, operation, and control. However, no study was found using machine learning in building commissioning. There are successful pilot studies on fault detection and diagnosis of HVAC equipment and systems, load prediction, energy baseline estimate, load shape clustering, occupancy prediction, and learning occupant behaviors and energy use patterns. None of the existing studies were adopted broadly by the building industry, due to common challenges including (1) lack of large scale labeled data to train and validate the model, (2) lack of model transferability, which limits a model trained with one data-rich building to be used in another building with limited data, (3) lack of strong justification of costs and benefits of deploying machine learning, and (4) the performance might not be reliable and robust for the stated goals, as the method might work for some buildings but could not be generalized to others. Findings from the study can inform future machine learning research to improve occupant comfort, energy efficiency, demand flexibility, and resilience of buildings, as well as to inspire young researchers in the field to explore multidisciplinary approaches that integrate building science, computing science, data science, and social science