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Advance knowledge of occupancy in commercial buildings facilitates implementation of occupant-centric control schemes that reduce energy use and increase comfort. However, training and validation of occupancy prediction models can be challenging since ground truth data is not always easily obtainable. In fact, not only is the collection of ground truth costly because of the manual labor involved, it might be restricted in time and space for security and privacy reasons. As a result, prediction based on semi-supervised learning techniques using limited ground truth data can be a promising approach with a slight compromise on accuracy. In this paper, an innovative method for day-ahead prediction of total building occupancy is proposed which leverages the opportunistic probing signals from a WiFi network. Using only two days of ground truth occupancy data, a model based on a combination of linear regression and artificial neural networks is able to predict day-ahead occupancy count with 90 percent accuracy., 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE), 22-26 August 2019, Vancouver, BC, Canada

© 2018 Elsevier Ltd With the rapid advances in sensing and digital technologies, cyber-physical systems are regarded as the most viable platforms for improving building design and management. Researchers investigated the possibility of integrating energy management systems with cyber-physical systems to form energy-cyber-physical systems in order to promote building energy management. However, minimizing energy consumption while fulfilling building functions for energy-cyber-physical systems is challenging due to the dynamics of building occupants. As occupant behavior is a major source of uncertainty for energy management, ignoring it often results in both energy waste caused by overheating and overcooling as well as discomfort due to insufficient thermal and ventilation services. To mitigate such uncertainties, this study proposes an occupancy-linked energy-cyber-physical system that incorporates WiFi probe-based occupancy detection. The proposed framework utilizes ensemble classification algorithms to extract three forms of occupancy information. It creates a data interface to link energy management systems and cyber-physical systems and allows for automated occupancy detection and interpretation by assembling multiple weak classifiers for WiFi signals. A validation experiment in a large office room was conducted to examine the performance of the proposed occupancy-linked energy-cyber-physical systems. The experiment and simulation results suggest that, with a proper classifier and occupancy data type, the proposed model can potentially save about 26.4% of energy consumption in cooling and ventilation demands.

This paper presents the part of the research that has been done at the Universities both in Belgrade and Thessaloniki, Greece, taking into account indoor environmental quality in office buildings and classrooms. The measurements that are presented were done in Process Equipment Design Laboratory at Aristotle University Thessaloniki, during March 2015. Indoor environmental quality regarding air temperature, relative humidity, and CO2 concentration in two representative offices is observed. The similar offices are located one on the north-east and the other one on the south-west side of the University building, so as to be representative of the orientation’s impact. Furthermore, the impact of natural ventilation on CO2 concentration and temperature is monitored, together with the offices’ occupancy. Recommended parameters for indoor air quality are compared and discussed on the base of several standards: SRPS EN 15251:2010, ASHRAE standards 55 and 62.1, and ISO 7730. The main objectives, as set from these standards are discussed, together with the investigation results. [Projekat Ministarstva nauke Republike Srbije, br. 33047]

Building energy systems are designed to handle both permanent and temporary occupants. Permanent occupants are considered the base energy load while temporary occupants are considered a temporary or additional load. Temporary occupancy is potentially the most difficult to design as the number of temporary occupants varies more significantly than permanent occupants. This case study was designed to investigate the effect of occupancy on energy loads, i.e. the relationship between occupancy and building energy loads. This study estimated the building occupancy by using existing network infrastructure, such as Wi-Fi and wired Ethernet based on the assumption that the number of Wi-Fi connections and the wired Ethernet traffic were used as a proxy for total and stationary occupancy. The relationships were then examined using correlations and regression analyses. The results showed the following: 1. Stationary occupancy was successfully estimated using the network infrastructure, 2. There was a linear relationship between electricity use and total occupancy (and, thus, the use of network infrastructure), 3. Permanent occupants generated a higher impact on the electricity load than the temporary occupants, 4. There was a logarithmic relationship between electricity use and the Ethernet data traffic (a proxy of permanent occupants), and 5. The statistical and qualitative analyses indicated that there was no significant relationship between occupancy and thermal loads, such as cooling and heating loads.

The number of occupants in a whole building, a zone or a room is an important parameter when improving the energy efficiency of a building. Using camera-based 3D or thermal sensors to detect passing of count lines are becoming more and more common. However, such sensors are not perfect and errors add up over time. In this paper we present the PLCount algorithm to accurately estimate total building, zone or room counts by fusing data from multiple count lines. The algorithm applies probabilistic reasoning together with occupancy constraints to accurately estimate the total number of occupants. We evaluate the algorithm on two data-sets from a small and a large office building. The evaluation shows a considerably lower RMSE compared to the raw counts and naive correction approach with up to 86% and 70% error reduction respectively and similarity analysis of consecutive weeks demonstrate the stability of the algorithm over time. We also demonstrate the use of the data for analyzing the energy consumption of a building. By presenting more accurate algorithms for estimating total occupancy we hope to enable buildings to better serve the actual number of occupants to improve comfort and energy efficiency of buildings.

Part 11: New Methods and Tools for Big Data Wokshop (MT4BD); International audience; A novel Building Multi-occupancy Analysis & Visualization through Data Intensive Processing techniques is going to be presented in this paper. Building occupancy monitoring plays an important role in increasing energy efficiency and provides useful semantic information about the usage of different spaces and building performance generally. In this paper the occupancy extraction subsystem is constituted by a collection of depth image cameras and a multi-sensorial cloud (utilizing big data from various sensor types) in order to extract the occupancy per space. Furthermore, a number of novel visual analytics techniques allow the end-users to process big data in different temporal resolutions in a compact and comprehensive way taking into account properties of human cognition and perception, assisting them to detect patterns that may be difficult to be detected otherwise. The proposed building occupancy analysis system has been tested and applied to various spaces of CERTH premises with different characteristics in a real-life testbed environment.