A cluster analysis approach to sampling domestic properties for sensor deployment

This is the final version. Available on open access from Elsevier via the DOI in this recordData availability: Data will be made available on request.Sensors are an increasingly widespread tool for monitoring utility usage (e.g., electricity) and environmental data (e.g., temperature). In large-scale projects, it is often impractical and sometimes impossible to place sensors at all sites of interest, for example due to limited sensor numbers or access. We test whether cluster analysis can be used to address this problem. We create clusters of potential sensor sites using factors that may influence sensor measurements. The clusters provide groups of sites that are similar to each other, and that differ between groups. Sampling a few sites from each group provides a subset that captures the diversity of sites. We test the approach with two types of sensors: utility usage (gas and water) and outdoor environment. Using a separate analysis for each sensor type, we create clusters using characteristics from up to 298 potential sites. We sample across these clusters to provide representative coverage for sensor installations. We verify the approach using data from the sensors installed as a result of the sampling, as well as using other sensor measures from all available sites over one year. Results show that sensor data vary across clusters, and vary with the factors used to create the clusters, thereby providing evidence that this cluster-based approach captures differences across sensor sites. This novel methodology provides representative sampling across potential sensor sites. It is generalisable to other sensor types and to any situation in which influencing factors at potential sites are known. We also discuss recommendations for future sensor-based large-scale projects.European Regional Development Fund (ERDF)Southwest Academic Health Science NetworkCornwall Counci

Enabling Human Centric Smart Campuses via Edge Computing and Connected Objects

Early definitions of Smart Building focused almost entirely on the technology aspect and did not suggest user interaction at all. Indeed, today we would attribute it more to the concept of the automated building. In this sense, control of comfort conditions inside buildings is a problem that is being well investigated, since it has a direct effect on users’ productivity and an indirect effect on energy saving. Therefore, from the users’ perspective, a typical environment can be considered comfortable, if it’s capable of providing adequate thermal comfort, visual comfort and indoor air quality conditions and acoustic comfort. In the last years, the scientific community has dealt with many challenges, especially from a technological point of view. For instance, smart sensing devices, the internet, and communication technologies have enabled a new paradigm called Edge computing that brings computation and data storage closer to the location where it is needed, to improve response times and save bandwidth. This has allowed us to improve services, sustainability and decision making. Many solutions have been implemented such as smart classrooms, controlling the thermal condition of the building, monitoring HVAC data for energy-efficient of the campus and so forth. Though these projects provide to the realization of smart campus, a framework for smart campus is yet to be determined. These new technologies have also introduced new research challenges: within this thesis work, some of the principal open challenges will be faced, proposing a new conceptual framework, technologies and tools to move forward the actual implementation of smart campuses. Keeping in mind, several problems known in the literature have been investigated: the occupancy detection, noise monitoring for acoustic comfort, context awareness inside the building, wayfinding indoor, strategic deployment for air quality and books preserving

Machine Learning and Deep Learning Methods for Enhancing Building Energy Efficiency and Indoor Environmental Quality – A Review

The built environment sector is responsible for almost one-third of the world's final energy consumption. Hence, seeking plausible solutions to minimise building energy demands and mitigate adverse environmental impacts is necessary. Artificial intelligence (AI) techniques such as machine and deep learning have been increasingly and successfully applied to develop solutions for the built environment. This review provided a critical summary of the existing literature on the machine and deep learning methods for the built environment over the past decade, with special reference to holistic approaches. Different AI-based techniques employed to resolve interconnected problems related to heating, ventilation and air conditioning (HVAC) systems and enhance building performances were reviewed, including energy forecasting and management, indoor air quality and occupancy comfort/satisfaction prediction, occupancy detection and recognition, and fault detection and diagnosis. The present study explored existing AI-based techniques focusing on the framework, methodology, and performance. The literature highlighted that selecting the most suitable machine learning and deep learning model for solving a problem could be challenging. The recent explosive growth experienced by the research area has led to hundreds of machine learning algorithms being applied to building performance-related studies. The literature showed that existing research studies considered a wide range of scope/scales (from an HVAC component to urban areas) and time scales (minute to year). This makes it difficult to find an optimal algorithm for a specific task or case. The studies also employed a wide range of evaluation metrics, adding to the challenge. Further developments and more specific guidelines are required for the built environment field to encourage best practices in evaluating and selecting models. The literature also showed that while machine and deep learning had been successfully applied in building energy efficiency research, most of the studies are still at the experimental or testing stage, and there are limited studies which implemented machine and deep learning strategies in actual buildings and conducted the post-occupancy evaluation

Smart Monitoring and Control in the Future Internet of Things

The Internet of Things (IoT) and related technologies have the promise of realizing pervasive and smart applications which, in turn, have the potential of improving the quality of life of people living in a connected world. According to the IoT vision, all things can cooperate amongst themselves and be managed from anywhere via the Internet, allowing tight integration between the physical and cyber worlds and thus improving efficiency, promoting usability, and opening up new application opportunities. Nowadays, IoT technologies have successfully been exploited in several domains, providing both social and economic benefits. The realization of the full potential of the next generation of the Internet of Things still needs further research efforts concerning, for instance, the identification of new architectures, methodologies, and infrastructures dealing with distributed and decentralized IoT systems; the integration of IoT with cognitive and social capabilities; the enhancement of the sensing–analysis–control cycle; the integration of consciousness and awareness in IoT environments; and the design of new algorithms and techniques for managing IoT big data. This Special Issue is devoted to advancements in technologies, methodologies, and applications for IoT, together with emerging standards and research topics which would lead to realization of the future Internet of Things

Rakennusten energiasuorituskyvyn parantaminen hyödyntämällä saatavilla olevaa dataa

The objective of this research is to find out how the energy performance of buildings can be improved effectively by exploiting available data in the operations and maintenance phase. In this research building automation systems, open data and Internet of Things are studied as value generating technological solutions. The research process is based on reviewing research literature, conducting interviews and analyzing measurement data recorded by the building automation system of a case office building. The study identified 11 initiatives to close existing energy performance gaps. These initiatives belonged to categories of developing building services equipment control, increasing the extent of available data, observing the state of user experience and facilitating maintenance processes. Then the three most effective were chosen for a feasibility study. This effectiveness of an initiative was judged by evaluating it in the dimensions of expected benefits and challenge to implement in an indicative manner by 13 interviewees in four stakeholder groups. This simple evaluation method turned out to serve its purpose well: Vague evaluation dimensions covered both quantitative and qualitative aspects, while stakeholder groups had different perspectives on the initiatives. Thus the method is recommended for similar problems, as long as only indicative results are pursued. Out of the 11 initiatives, the most effective ones were considered to be those that are simple and do not require any installation work, or at the most the installation of transmitters or sensors: 1) adaptive heating control, 2) user satisfaction measurement systems, 3) energy performance monitoring systems and 4) selected equipment group control interfaces. Feasibility studies suggested that adaptive heating control has the potential to increase energy performance with negligible installation work, user satisfaction measurement system would be sensible to pilot as a service, energy efficiency monitoring in small-scale would be convenient to purchase as a service and selected group control interfaces enable large savings with small trouble. The least effective initiatives were considered to be the ones that are complex, risk user satisfaction or require the integration of numerous systems.Tämän tutkimuksen tavoitteena on selvittää miten rakennusten energiasuorituskykyä voidaan parantaa tehokkaasti hyödyntämällä saatavilla olevaa dataa käyttö- ja ylläpitovaiheessa. Tutkimuksessa arvoa tuottavina teknologisina osaratkaisuina tutkitaan rakennusautomaatiojärjestelmiä, avointa dataa ja Esineiden Internetiä. Tutkimusmenetelminä käytetään kirjallisuustutkimusta, haastatteluja ja toimistorakennuksen rakennusautomaatiojärjestelmän tuottaman mittaustiedon analysointia. Tutkimus tuotti 11 energiasuorituskyvyn ongelmakohtien korjaamiseen tähtäävää aloitetta. Näiden aloitteiden päämäärinä oli taloteknisten laitteiden ohjauksen kehittäminen, käytettävissä olevan datan lisääminen, käyttäjätyytyväisyyden tarkkailu tai ylläpidon prosessien helpottaminen. Aloitteista kolmelle tehokkaimmiksi arvioiduille tehtiin tarkempi toteutettavuustutkimus. Tehokkuusarviointi perustui neljään sidosryhmään jaetun 13 haastateltavan suuntaa-antavaan näkemykseen aloitteiden toimeenpanon hyödyistä ja haasteista. Tämä yksinkertainen arviointitapa osoittautui toimivaksi: Moniselitteiset arviointiulottuvuudet kattoivat sekä määrälliset että laadulliset näkökulmat, kun taas eri sidosryhmät painottivat aloitteiden eri ominaisuuksia. Näin ollen kyseinen arviointimenetelmä soveltuu samankaltaisiin ongelmiin, kunhan tulosten suuntaa-antava taso on riittävä tutkimuksen tavoitteisiin nähden. Näistä 11 aloitteesta tehokkaimmiksi koettiin pääosin sellaiset, jotka ovat yksinkertaisia eivätkä vaadi laajaa asennustyötä: 1) adaptiivinen lämmityksen säätö, 2) käyttäjätyytyväisyyden mittausjärjestelmä, 3) energiatehokkuuden seurantajärjestelmä ja 4) laitteiden ryhmähallintaan perustuvat käyttöliittymät. Toteutettavuustutkimusten perusteella adaptiivinen lämmityksensäätö voisi parantaa energiasuorituskykyä pienellä asennustyöllä, käyttäjätyytyväisyyden mittausjärjestelmää olisi järkevää aluksi kokeilla palveluna, energiatehokkuuden seuranta pienessä mittakaavassa olisi kätevää ostaa palveluna ja ryhmäohjaukseen perustuvat käyttöliittymät voisivat säästää huomattavasti energiaa pienellä vaivalla. Tehottomimmiksi arvioidut aloitteet olivat monimutkaisia, vaaransivat käyttäjätyytyväisyyden tai vaativat useiden järjestelmien yhteensovittamista

Development and characterization of sensors for human health

L'abstract è presente nell'allegato / the abstract is in the attachmen

Simulation of site-specific irrigation control strategies with sparse input data

Crop and irrigation water use efficiencies may be improved by managing irrigation application timing and volumes using physical and agronomic principles. However, the crop water requirement may be spatially variable due to different soil properties and genetic variations in the crop across the field. Adaptive control strategies can be used to locally control water applications in response to in-field temporal and spatial variability with the aim of maximising both crop development and water use efficiency. A simulation framework ‘VARIwise’ has been created to aid the development, evaluation and management of spatially and temporally varied adaptive irrigation control strategies (McCarthy et al., 2010). VARIwise enables alternative control strategies to be simulated with different crop and environmental conditions and at a range of spatial resolutions. An iterative learning controller and model predictive controller have been implemented in VARIwise to improve the irrigation of cotton. The iterative learning control strategy involves using the soil moisture response to the previous irrigation volume to adjust the applied irrigation volume applied at the next irrigation event. For field implementation this controller has low data requirements as only soil moisture data is required after each irrigation event. In contrast, a model predictive controller has high data requirements as measured soil and plant data are required at a high spatial resolution in a field implementation. Model predictive control involves using a calibrated model to determine the irrigation application and/or timing which results in the highest predicted yield or water use efficiency. The implementation of these strategies is described and a case study is presented to demonstrate the operation of the strategies with various levels of data availability. It is concluded that in situations of sparse data, the iterative learning controller performs significantly better than a model predictive controller

Air pollution and livestock production

The air in a livestock farming environment contains high concentrations of dust particles and gaseous pollutants. The total inhalable dust can enter the nose and mouth during normal breathing and the thoracic dust can reach into the lungs. However, it is the respirable dust particles that can penetrate further into the gas-exchange region, making it the most hazardous dust component. Prolonged exposure to high concentrations of dust particles can lead to respiratory health issues for both livestock and farming staff. Ammonia, an example of a gaseous pollutant, is derived from the decomposition of nitrous compounds. Increased exposure to ammonia may also have an effect on the health of humans and livestock. There are a number of technologies available to ensure exposure to these pollutants is minimised. Through proactive means, (the optimal design and management of livestock buildings) air quality can be improved to reduce the likelihood of risks associated with sub-optimal air quality. Once air problems have taken hold, other reduction methods need to be applied utilising a more reactive approach. A key requirement for the control of concentration and exposure of airborne pollutants to an acceptable level is to be able to conduct real-time measurements of these pollutants. This paper provides a review of airborne pollution including methods to both measure and control the concentration of pollutants in livestock buildings

The Role of Occupants in Buildings’ Energy Performance Gap: Myth or Reality?

Buildings’ expected (projected, simulated) energy use frequently does not match actual observations. This is commonly referred to as the energy performance gap. As such, many factors can contribute to the disagreement between expectations and observations. These include, for instance, uncertainty about buildings’ geometry, construction, systems, and weather conditions. However, the role of occupants in the energy performance gap has recently attracted much attention. It has even been suggested that occupants are the main cause of the energy performance gap. This, in turn, has led to suggestions that better models of occupant behavior can reduce the energy performance gap. The present effort aims at the review and evaluation of the evidence for such claims. To this end, a systematic literature search was conducted and relevant publications were identified and reviewed in detail. The review entailed the categorization of the studies according to the scope and strength of the evidence for occupants’ role in the energy performance gap. Moreover, deployed calculation and monitoring methods, normalization procedures, and reported causes and magnitudes of the energy performance gap were documented and evaluated. The results suggest that the role of occupants as significant or exclusive contributors to the energy performance gap is not sufficiently substantiated by evidence.</jats:p