Pedestrian Counting Based on Piezoelectric Vibration Sensor

Pedestrian counting has attracted much interest of the academic and industry communities for its widespread application in many real-world scenarios. While many recent studies have focused on computer vision-based solutions for the problem, the deployment of cameras brings up concerns about privacy invasion. This paper proposes a novel indoor pedestrian counting approach, based on footstep-induced structural vibration signals with piezoelectric sensors. The approach is privacy-protecting because no audio or video data is acquired. Our approach analyzes the space-differential features from the vibration signals caused by pedestrian footsteps and outputs the number of pedestrians. The proposed approach supports multiple pedestrians walking together with signal mixture. Moreover, it makes no requirement about the number of groups of walking people in the detection area. The experimental results show that the averaged F1-score of our approach is over 0.98, which is better than the vibration signal-based state-of-the-art methods.Peer reviewe

Save Money or Feel Cozy?: A Field Experiment Evaluation of a Smart Thermostat that Learns Heating Preferences

We present the design of a fully autonomous smart thermostat that supports end-users in managing their heating preferences in a realtime pricing regime. The thermostat uses a machine learning algorithm to learn how a user wants to trade off comfort versus cost. We evaluate the thermostat in a field experiment in the UK involving 30 users over a period of 30 days. We make two main contributions. First, we study whether our smart thermostat enables end-users to handle real-time prices, and in particular, whether machine learning can help them. We find that the users trust the system and that they can successfully express their preferences; overall, the smart thermostat enables the users to manage their heating given real-time prices. Moreover, our machine learning-based thermostats outperform a baseline without machine learning in terms of usability. Second, we present a quantitative analysis of the users’ economic behavior, including their reaction to price changes, their price sensitivity, and their comfort-cost trade-offs. We find a wide variety regarding the users’ willingness to make trade-offs. But in aggregate, the users’ settings enabled a large amount of demand response, reducing the average energy consumption during peak hours by 38%

An Occupancy Based Cyber-Physical System Design for Intelligent Building Automation

Cyber-physical system (CPS) includes the class of Intelligent Building Automation System (IBAS) which increasingly utilizes advanced technologies for long term stability, economy, longevity, and user comfort. However, there are diverse issues associated with wireless interconnection of the sensors, controllers, and power consuming physical end devices. In this paper, a novel architecture of CPS for wireless networked IBAS with priority-based access mechanism is proposed for zones in a large building with dynamically varying occupancy. Priority status of zones based on occupancy is determined using fuzzy inference engine. Nondominated Sorting Genetic Algorithm-II (NSGA-II) is used to solve the optimization problem involving conflicting demands of minimizing total energy consumption and maximizing occupant comfort levels in building. An algorithm is proposed for power scheduling in sensor nodes to reduce their energy consumption. Wi-Fi with Elimination-Yield Nonpreemptive Multiple Access (EY-NPMA) scheme is used for assigning priority among nodes for wireless channel access. Controller design techniques are also proposed for ensuring the stability of the closed loop control of IBAS in the presence of packet dropouts due to unreliable network links

Wireless Sensor Network para mejorar la eficiencia energética en hogares y pymes y su integración en la Smart Grid

[SPA] Durante los últimos años los ámbitos tecnológicos de la Redes de Sensores Inalámbricas – WSN –y la aplicación de las tecnologías de la información y comunicación sobre la redes eléctricas – Smart Grid – se han convertido en importantes campos de interés científico. De igual forma, la mejora de la Eficiencia Energética se ha convertido en una necesidad de la sociedad actual, debido al volumen de crecimiento de la población, y por lo tanto la demanda, y la dependencia energética actual de los combustibles fósiles. Esta Tesis analiza en profundidad las diferentes posibilidades de aplicación de las WSN en el control energético de bajo voltaje, a través del diseño e implementación de un sistema, de bajo coste y de fácil implantación, capaz de optimizar el consumo energético en hogares y pymes. El sistema descrito cuenta con una unidad de almacenamiento energético de respaldo que ayuda a minimizar la potencia pico requerida en la instalación, ayudando a balancear la curva de consumo del hogar o empresa. Este sistema opera conjuntamente con la WSN como un nodo más, haciendo la función de Gateway con la nube a la vez que aporta control e información a los usuarios de forma que estos puedan adoptar medidas por su parte en relación al uso que hacen de la energía. Para dotar al sistema de capacidad de interoperación con otros sistemas y hacerlo accesible desde Internet en general se le ha dotado de capacidad de comunicación basada en IPv6 haciendo que la red pase a formar parte de lo que se ha dado a conocer como Internet de las Cosas – Internet of Things, IoT–. De igual forma se expone la posibilidad de realizar una comunicación bidireccional con la productora eléctrica de forma que se obtenga información sobre las variaciones del precio de la electricidad que se consume, así como la posibilidad de reportar previsiones y modelos de demanda en tiempo real a la misma de forma que se acerca al concepto de Demanda Gestionada por el Hogar – Home Energy Management, HEM –.[ENG] In last years the technological knowledge areas of Wireless Sensor Networks – WSN – and the Smart Grid – Information and Communications Technology application over the electrical transportation network – became scientific areas of great interest. Similarly, the improvement on Energy Efficiency became a necessity in actual social context, due to the high population growth volume, and therefore the energy demand, and the actual energy dependency on fossil fuels. This Thesis deeply analyses the different possibilities of WSN application on the low voltage energy control, through the design and implementation of a low cost system, easy to deploy, and able to optimise the energy consumption in households and SMEs. The described system has a back-up energy storage unit that helps to minimise the peak power required in the installation. It also helps to balance the demand curve in the household or SME. This system jointly operates with the WSN as a extra node, making the role of Gateway with the cloud, at the same time it provides control capabilities and information to final users. This give final users the capacity of take measures relatives to the way they use the energy. The system has a communication protocol based in IPv6 in order to provide it with interoperation capacity with other system and with access from Internet devices. In this way, all the nodes in WSN are included in the Internet of Things. Finally, the possibility of perform a bidirectional communication with the Utility to obtain prices changes information from the Utility and the possibility of report back foreseen and demand models in real time to the Utility. It approaches the system to the concept of Home Energy Management –HEM –.Escuela Internacional de DoctoradoUniversidad Politécnica de CartagenaPrograma de Doctorado Tecnologías de la Información y las Comunicacione

Thermovote: Participatory sensing for efficient building hvac conditioning

Abstract Thermal comfort has traditionally been measured solely by temperature. While other methods such as Predicted Mean Vote (PMV) are available for measuring thermal comfort, the parameters required for an accurate value are overly complicated to obtain and require a great deal of sensory input. This paper proposes to bypass overly cumbersome or simplistic measures thermal comfort by bringing humans in the loop. By using humans as sensors, we can accurately adjust temperatures to improve occupant comfort. We show that occupants are more comfortable with a system that continually adjusts to thermal preference than a system that attempts to predict user comfort based on environmental factors. In addition, we also show that such a system is able to save 10.1% energy while improving the quality of service

Exploring Energy, Comfort, and Building Health Impacts of Deep Setback and Normal Occupancy Smart Thermostat Implementation

As smart thermostat adoption rates continue to increase, it becomes worthwhile to explore what unanticipated outcomes may result in their use. Specific attention was paid to smart thermostat impacts to deep setback and normal occupancy states in a variety of conditions while complying with the ventilation and temperature requirements of ASHRAE 90.2-2013. Custom weather models and occupancy schedules were generated to efficiently explore a combination of weather conditions, building constructions, and occupancy states. The custom modeling approach was combined with previous experimental data within the Openstudio graphics interface to the EnergyPlus building modeling engine. Results indicate smart thermostats add the most value to winter deep setback conditions while complying with ASHRAE 90.2. Major potential humidity issues were identified when complying with ASHRAE 90.2 during cooling season. It also appears smart thermostats add little value to occupants when complying with ASHRAE 90.2 during cooling season across multiple climates and building constructions. Further exploration into humidity issues identified are required, as well as refining the energy model and moving towards real-world validation

A review of household water demand management and consumption measurement

Rapid population growth and economic prosperity among other factors are exacerbating existing water stress in the east and southeast regions of England, hence, the water sector is increasingly shifting focus from the expansion of water sources and increased abstraction to demand-side management (DSM) strategies aimed at improving household water efficiency and reducing per capita consumption. A crucial component of water DSM strategy is a good understanding of household water use patterns and the myriad factors that influence them. Smart metering, conflated with innovative techniques and groundbreaking ancillaries continue to support DSM strategies by providing quasi-real-time data, offering powerful insights into household water consumption patterns and delivering behaviour-changing feedback to consumers. This paper presents a comprehensive review of the current state of household water consumption and their determinants as reported in the literature. The paper also reviews the methods and techniques for measuring and understanding consumption patterns and discuss prominent DSM instruments utilised in the household water demand sector globally along with their relative impact on per capita consumption (PCC). The review concludes that while disaggregation remains a very effective means of revealing consumption patterns at micro-component levels, the process is time-consuming and costly, relying on high-resolution data, specific hardware and software combination, making it difficult to incorporate into the utility’s routine DSM framework. A future research is proposed, that may focus on an alternative, scalable consumption pattern recognition approach that can easily be incorporated into the utility’s DSM strategy using medium resolution smart-meter data

Energy Data Analytics for Smart Meter Data

The principal advantage of smart electricity meters is their ability to transfer digitized electricity consumption data to remote processing systems. The data collected by these devices make the realization of many novel use cases possible, providing benefits to electricity providers and customers alike. This book includes 14 research articles that explore and exploit the information content of smart meter data, and provides insights into the realization of new digital solutions and services that support the transition towards a sustainable energy system. This volume has been edited by Andreas Reinhardt, head of the Energy Informatics research group at Technische Universität Clausthal, Germany, and Lucas Pereira, research fellow at Técnico Lisboa, Portugal

Analyzing Enterprise WiFi Session Data for Modeling Building Occupancy, Evacuation, and Energy Consumption

Buildings are the prime components of office complexes, university campuses, and city centers. They are expensive to build and expensive to operate. Building managers are under constant pressure to keep them efficient and safe. However, they are often stymied by lack of fine-grained data that can help them optimize occupancy levels so as to make most efficient use of space, evacuation patterns that can ensure safety in the event of emergencies, and energy usage behavior that can help reduce operating costs. While several modern buildings are increasingly being equipped with sensors for detecting people presence, movement patterns, and thermal conditions, such instrumentation can often be expensive and limited in scale. This thesis investigates the potential to use data generated by the pervasive WiFi infrastructure that is present in all buildings. Specifically, we evaluate the use of WiFi data to model room usage, anatomize emergency evacuations, and reduce energy excursion costs associated with evacuation events. We begin this thesis by surveying data-driven approaches for efficient building operation and management, while reviewing existing technologies for measuring occupancy using both existing and purpose-built sensing infrastructure. Central to this thesis is the data we have collected and analyzed on WiFi session logs from a dense wireless network consisting of nearly 5000 access points across 50 buildings in a large university campus over a period of 2 years. For our first contribution, we use this data to develop a machine learning-based method to estimate classroom occupancy in near real-time. The output of our method is compared to that from specialized people-counting sensors, and the symmetric Mean Absolute Percentage Error is no more than 13%. Our second contribution develops a systematic method to evaluate emergency evacuation events using building WiFi session data. Our systematic analysis of 43 planned and unplanned evacuation events across 14 buildings quantifies important measures such as evacuation speed, number of evacuees, and typicality of occupancy levels, demonstrating that WiFi data enables accurate and scalable evaluation of building evacuations, corroborating current manual records and revealing new insights. For our third and final contribution, we show that evacuations (particularly during summer) can result in HVAC power excursions of up to 150% above the agreed threshold, imposing heavy power tariffs. We develop a cooling strategy that allows the power cost to be traded off against thermal comfort of occupants post evacuation in a tunable manner. Application of our algorithm to typical building evacuation scenarios shows that the power excursion costs can be largely mitigated for as little as 5 minutes of delay in achieving ideal indoor temperatures. Taken together, our contributions equip building operators with tools and techniques to improve efficiency and safety by leveraging existing WiFi data with no additional infrastructure costs

Modelling and optimisation of resource usage in an IoT enabled smart campus

University campuses are essentially a microcosm of a city. They comprise diverse facilities such as residences, sport centres, lecture theatres, parking spaces, and public transport stops. Universities are under constant pressure to improve efficiencies while offering a better experience to various stakeholders including students, staff, and visitors. Nonetheless, anecdotal evidence indicates that campus assets are not being utilized efficiently, often due to the lack of data collection and analysis, thereby limiting the ability to make informed decisions on the allocation and management of resources. Advances in the Internet of Things (IoT) technologies that can sense and communicate data from the physical world, coupled with data analytics and Artificial intelligence (AI) that can predict usage patterns, have opened up new opportunities for organizations to lower cost and improve user experience. This thesis explores this opportunity via theory and experimentation using UNSW Sydney as a living laboratory. The building blocks of this thesis consist of three pillars of execution, namely, IoT deployment, predictive modelling, and optimization. Together, these components create an end-to-end framework that provides informed decisions to estate manager in regards to the optimal allocation of campus resources. The main contributions of this thesis are three application domains, which lies on top of the execution pillars, defining campus resources as classrooms, car parks, and transit buses. Specifically, our contributions are: i) We evaluate several IoT occupancy sensing technologies and instrument 9 lecture halls of varying capacities with the most appropriate sensing solution. The collected data provides us with insights into attendance patterns, such as cancelled lectures and class tests, of over 250 courses. We then develop predictive models using machine learning algorithms and quantile regression technique to predict future attendance patterns. Finally, we propose an intelligent optimisation model that allows allocations of classes to rooms based on the dynamics of predicted attendance as opposed to static enrolment number. We show that the data-driven assignment of classroom resources can achieve a potential saving in room cost of over 10\% over the course of a semester, while incurring a very low risk of disrupting student experience due to classroom overflow; ii) We instrument a car park with IoT sensors for real-time monitoring of parking demand and comprehensively analyse the usage data spanning over 15 months. We then develop machine learning models to forecast future parking demand at multiple forecast horizons ranging from 1 day to 10 weeks, our models achieve a mean absolute error (MAE) of 4.58 cars per hour. Finally, we propose a novel optimal allocation framework that allows campus manager to re-dimension the car park to accommodate new paradigms of car use while minimizing the risk of rejecting users and maintaining a certain level of revenue from the parking infrastructure; iii) We develop sensing technology for measuring an outdoor orderly queue using ultrasonic sensor and LoRaWAN, and deploy the solution at an on campus bus stop. Our solution yields a reasonable accuracy with MAE of 10.7 people for detecting a queue length of up to 100 people. We then develop an optimisation model to reschedule bus dispatch times based on the actual dynamics of passenger demand. The result suggests that a potential wait time reduction of 42.93% can be achieved with demand-driven bus scheduling. Taken together, our contributions demonstrates that there are significant resource efficiency gains to be realised in a smart-campus that employs IoT sensing coupled with predictive modelling and dynamic optimisation algorithms