PreCount: a predictive model for correcting real-time occupancy count data

Abstract Sensing the number of people occupying a building in real-time facilitates a number of pervasive applications within the area of building energy optimization and adaptive control. To ascertain occupant counts, the adoption of camera-based sensors i.e. 3D stereo-vision and thermal cameras have grown significantly. However, camera-based sensors can only produce occupant counts with accumulating errors. Existing methods for correcting such errors can only correct erroneous count data at the end of the day and not in real-time. However, many applications depend on real-time corrected counts. In this paper, we present an algorithm named PreCount for accurately correcting raw counts in real-time. The core idea of PreCount is to learn error estimates from the past. We evaluated the accuracy of the PreCount algorithm using datasets from four buildings. Also, the Normalized Root Mean Squared Error was used to evaluate the performance of PreCount. Our evaluation results show that in real-time PreCount achieved a significantly lower Normalized Root Mean Squared Error compared to raw counts and other correction approach with a maximum error reduction of 68% when benchmarked with ground truth data. By presenting a more accurate algorithm for estimating occupant counts in real-time, we hope to enable buildings to better serve the actual number of people to improve both occupant comfort and energy efficiency

Current practices and infrastructure for open data based research on occupant-centric design and operation of buildings

Many new tools for improving the design and operation of buildings try to realize the potential of big data. In particular, data is an important element for occupant-centric design and operation as occupants’ presence and actions are affected by a high degree of uncertainty and, hence, are hard to model in general. For such research, data handling is an important challenge, and following an open science paradigm based on open data can increase efficiency and transparency of scientific work. This article reviews current practices and infrastructure for open data-driven research on occupant-centric design and operation of buildings. In particular, it covers related work on open data in general and for the built environment in particular, presents survey results for existing scientific practices, reviews technical solutions for handling data and metadata, discusses ethics and privacy protection and analyses principles for the sharing of open data. In summary, this study establishes the status quo and presents an outlook on future work for methods and infrastructures to support the open data community within the built environment