Implicit sensing of building occupancy count with information and communication technology data sets

Occupancy count, i.e., the number of people in a space or building, is becoming an increasingly important measurement to model, predict, and minimize operational energy consumption. Explicit, hardware-based, occupancy counters have been proposed but wide scale adoption is limited due to the cost and invasiveness of system implementation. As an alternative approach, researchers propose using data from existing information and communication technology (ICT) systems to infer occupancy counts. In the reported work, three different data streams, security access data, wireless connectivity data, and computer activity data, from ICT systems in a medium sized office building were collected and compared to the counts of a commercially available occupancy counter over 59 working days. The occupancy counts from the ICT systems are compared to the commercial counter with and without calibration to determine the ability of the data sets to measure occupancy. Various transformations were explored as calibration techniques for the ICT data sets. Training sets of 24, 48, and 120 hours were employed to determine how long an external calibration system would need to be installed. The analysis found that calibration is required to provide accurate counts. While each ICT data set provides similar magnitudes and time series behavior, incorporating all three data streams in a two layer neural network with 1 week of training data provides the most accurate estimates against 5 performance metrics. Whilst 1 week of data provides the best results, 24 hours is sufficient to develop similar levels of performance

A Conceptual Framework for Designing Interactive Human-Centred Building Spaces to Enhance User Experience in Specific-Purpose Buildings

Human/User interaction with buildings are mostly restricted to interacting with building automation systems through user-interfaces that mainly aim to improve energy efficiency of buildings and ensure comfort of occupants. This research builds on the existing theories of Human-Building Interaction (HBI) and proposes a novel conceptual framework for HBI that combines the concepts of Human-Computer Interaction (HCI) and Ambient Intelligence (AmI). The proposed framework aims to study the needs of occupants in specific-purpose buildings, which is currently undermined. Specifically, we explore the application of the proposed HBI framework to improve the learning experience of students in academic buildings. Focus groups and semi-structured interviews were conducted among students who are considered primary occupants of Goodwin Hall, a flagship smart engineering building at Virginia Tech. Qualitative coding and concept mapping were used to analyze the qualitative data and determine the impact of occupant-specific needs on the learning experience of students in academic buildings. The occupant-specific problem that was found to have the highest direct impact on learning experience was finding study space and highest indirect impact was Indoor Environment Quality (IEQ). We discuss new ideas for designing Intelligent User Interfaces (IUI), e.g. Augmented Reality (AR), increase the perceivable affordances for building occupants and considering a context-aware ubiquitous analytics-based strategy to provide services that are tailored to address the identified needs

Effectiveness of using WiFi technologies to detect and predict building occupancy

This paper presents findings of a case-study demonstrating the effectiveness of using WiFi networks to detect occupancy as opposed to CO2 sensors, commonly used for demand-controlled heating, ventilation and air conditioning (HVAC) systems. The study took place in one building at the University of Manitoba Fort Garry campus in Canada. In a classroom, the number of WiFi connections was collected on an hourly basis over one-week, simultaneously with CO2 concentration levels at 10-min intervals. The number of occupants in this classroom was also counted on an hourly basis over the same study period. Data analysis showed that WiFi counts predicted actual occupancy levels more accurately than CO2 concentration levels, thus validating the use of this technology to track occupancy. This study was the first to use both CO2 concentration and WiFi counts simultaneously as indicators for occupancy. Results demonstrated the possibility of using WiFi counts in large buildings for controlling HVAC systems at a higher accuracy and lower cost than other sensor technologies

Non-Intrusive Occupancy Detection Methods and Models

Occupants in the built environment impact facility energy consumption and indoor air quality. Predicting the presence of occupants within the built environment can therefore be used to manage these factors while providing additional benefits in terms of emergency management and future space utilization. Detecting occupancy requires a combination of sensors and models to accurate assess data collected within facilities to predict occupancy. This thesis investigated occupancy detection through a non-invasive data collection sensors and model. Specifically, this thesis sought to answer two research questions examining the ability of a radial basis function to accurately predict occupancy when generated from data collected from two facilities. Generated models were evaluated on the data from which they were derived, self-estimation, as well as applied to other areas within the same facility, cross-estimation. The motivation, sensors and models, were discussed to establish a framework. The principle implications of this research is to reduce energy consumption by knowing when the built environment is occupied through the use of non-invasive data collection sensors supplying inputs into a model. The resulting accuracy rates of the derived models ranged from 48% - 68% when using three collected parameters: temperature, relative humidity and carbon dioxide

Occupancy detection in non-residential buildings – A survey and novel privacy preserved occupancy monitoring solution

Buildings use approximately 40% of global energy and are responsible for almost a third of the worldwide greenhouse gas emissions. They also utilise about 60% of the world’s electricity. In the last decade, stringent building regulations have led to significant improvements in the quality of the thermal characteristics of many building envelopes. However, similar considerations have not been paid to the number and activities of occupants in a building, which play an increasingly important role in energy consumption, optimisation processes, and indoor air quality. More than 50% of the energy consumption could be saved in Demand Controlled Ventilation (DCV) if accurate information about the number of occupants is readily available (Mysen et al., 2005). But due to privacy concerns, designing a precise occupancy sensing/counting system is a highly challenging task. While several studies count the number of occupants in rooms/zones for the optimisation of energy consumption, insufficient information is available on the comparison, analysis and pros and cons of these occupancy estimation techniques. This paper provides a review of occupancy measurement techniques and also discusses research trends and challenges. Additionally, a novel privacy preserved occupancy monitoring solution is also proposed in this paper. Security analyses of the proposed scheme reveal that the new occupancy monitoring system is privacy preserved compared to other traditional schemes

An approach for building occupancy modelling considering the urban context

Building occupancy, which reflects occupant presence, movements and activities within the building space, is a key factor to consider in building energy modelling and simulation. Characterising complex occupant behaviours and their determinants poses challenges from the sensing, modelling, interpretation and prediction perspectives. Past studies typically applied time-dependent models to predict regular occupancy patterns for commercial buildings. However, this prevalent reliance on purely time-of-day effects is typically not sufficient to accurately characterise the complex occupancy patterns as they may vary with building’s surrounding conditions, i.e. the urban environment. Therefore, this research proposes a conceptual framework to incorporate the interactions between urban systems and building occupancy. Under the framework, we propose a novel modelling methodology relying on competing risk hazard formulation to analyse the occupancy of a case study building in London, UK. The occupancy profiles were inferred from the Wi-Fi connection logs extracted from the existing Wi-Fi infrastructure. When compared with the conventional discrete-time Markov Chain Model (MCM), the hazard-based modelling approach was able to better capture the duration dependent nature of the transition probabilities as well as incorporate and quantify the influence of the local environment on occupancy transitions. The work has demonstrated that this approach enables a convenient and flexible incorporation of urban dependencies leading to accurate occupancy predictions whilst providing the ability to interpret the impacts of urban systems on building occupancy. Keywords: Urban system; Competing risk hazard model; Building occupancy simulation; Wi4 Fi connection dat

Reaaliaikainen tilojen käytön seuranta: langaton sensoriverkko implementaatio

This paper discusses solutions and technologies for automatically assessing the state of space reservation in a building, e.g., a university or an office building. Improving space utilization can benefit an organization or a company in multiple aspects. These aspects include enhanced user experience and improved workflow, and minimized losses from maintaining unused space. Additionally, the space utilization information can be utilized lower electricity usage, by optimizing HVAC (Heating, ventilation, and air conditioning) and lighting systems. The goal of this study is to asses and compare different methods of gaining the real-time reservation status of a space. Both hardware and software factors are taken into account. The methods are first studied in the form of a literature review. This part of the study includes comparisons between relevant technologis. The most suitable solution is also inspected in practice. Furthermore, as part of the study a real-time reservation status monitoring system was developed utilizing Philips hardware and recommendable software technologies. This experimental phase of the study also includes an overview of testing and installing such a system. A system built by an outside supplier was also installed for reference. Both systems employ PIR (Passive Infrared) sensors. The sensors are not designed for people counting purposes, which was not a core focus of this study. Gathering space utilization data can have further, far stretching benefits. Therefore, this paper also discusses different use cases for the occupancy data. The value of such data can be significant, due to property costs taking up a large portion of companies’ expenses. Effectively utilizing this data can therefore prove to be remarkably advantageous.Tämä tutkimuspaperi käsittelee ratkaisuja ja teknologioita automaattiseen tilojen varausasteen määrittämiseen julkisissa rakennuksissa, kuten yliopistoissa tai toimistoissa. Tilojen käytön tehostaminen voi hyödyttää organisaatiota tai yritystä monilla tavoin. Näihin tapoihin kuuluvat muun muassa parantunut tilojen käyttökokemus ja työn sujuvuus, sekä minimoidut käyttämättömien tilojen ylläpitokustannukset. Lisäksi tilojen käyttösteinformaatiota voidaan hyödyntää sähkönkulutuksen alentamiseksi, optimoimalla LVI- ja valaistusjärjestelmiä. Tämän tutkimuksen tavoite on arvioida ja vertailla eri tapoja hankkia tieto tilojen reaaliaikaisesta varaustilanteesta. Sekä laitteisto, että ohjelmisto tekijät on otettu huomioon. Tapoja tutkitaan aluksi kirjallisuuskatsauksella. Tässä osassa tutkimusta vertaillaan eri teknologioita. Sopivimman ratkaisun toimintaa tutkittiin myös käytännössä Lisäksi osana tutkimusta kehitettiin reaaliaikaisen varaustilanteen seurantajärjestelmä hyödyntäen Philips Hue -laitteita ja suositeltavia ohjelmistoteknologioita. Tämä kokeellinen tutkimusvaihe sisältää myös yleiskuvan vastaavien järjestelmien testaamisesta ja asentamisesta. Myös kolmannen osapuolen toimittama järjestelmä asennettiin tiloihin vertailukohteeksi. Molemmat järjestelmät käyttävät PIR (Passive Infrared) sensoreita. Sensorit eivät ole tarkoitettu henkilölaskentaa varten, koska se ei ollut tämän tutkimuksen ydintavoite. Tilojen käyttödatan keräämisellä voi lisäksi olla muita, kauaskantoisia hyötyjä. Siksi tutkimuksessa käsitellään myös erilaisia tapoja hyötykäyttää sitä. Datan arvo voi olla merkittävä tilakustannusten ollessa merkittävä osa yritysten kuluja. Datan tehokas käyttö voi osoittautua huomattavan suotuisaksi

Tecnologías para la detección de ocupación en edificios

An essential part of the energy consumption of buildings is related to the occupant’s behavior. Which means that it is necessary to know more precisely how the user behaves and moves within the environments, and this is one of the challenges that require knowing the technology for occupancy monitoring in buildings. This article presents a summary of the technologies currently used for the control of building occupancy with the aim of investigating where potential future research can be directed, considering the current development.Una importante parte del consumo de energía de los edificios está relacionado con el comportamiento de los ocupantes. Lo que significa que es necesario conocer con mayor precisión cómo se comporta y se mueve el usuario dentro de los ambientes, y este es uno de los retos que requiere conocer los tipos de tecnologías para la detección de la ocupación en edificios. En este artículo presentamos un resumen de las tecnologías utilizadas en la actualidad para el control de la ocupación en edificios, de manera de tener un marco de referencia y además poder indagar hacia donde se pueden dirigir las investigaciones futuras, tomando en cuenta el desarrollo actual