Research & Projects

Name: Siva Subramanian Swaminathan

Project description
Heating, Ventilation and Air-Conditioning (HVAC) units in commercial buildings account for a huge portion of global energy consumption. There is an ever growing need to optimize the energy consumption of an HVAC system along with a system-of-subsystems entity that must be accurately integrated and controlled by the building automation system to ensure the occupants’ comfort with reduced energy consumption. To achieve these goals, it is necessary that accurate models be developed that describe the internal dynamics of the system to employ a satisfactory control architecture. This thesis work aims at provide sufficiently accurate models which are able to estimate the temperature, humidity and CO2 dynamics in an occupied room. A simplified linear model which describes the dynamics was developed by reformulating the physical equations into a linear regression format. This was followed by the employment of a suitable identification technique to estimate the physical parameters of the system. The second part of this thesis involves the formulation of a two level control architecture to optimize comfort and energy. In this work we propose a model-based framework to maximize the comfort of the occupants using the Predicted Mean Vote (PMV) index. In particular, the set-point control is based on a predictive controller based on a joint optimization of PMV and energy consumption; the low-level Proportional Integral HVAC controllers are autotuned based on simulations of a thermal model. A simulation based validation via a three room test case is presented: the results show the potential for good temperature tracking with a high degree of comfort while also reducing overall energy consumption.

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Name: Atul Pandaravila Biju

Project description
Description: Daylighting is the immediate exploitation of solar energy in the form of natural lighting and plays an integral role in minimizing the energy footprint of a building. Smart daylighting enables us to design buildings that provide comfort and energy savings. This work proposes a dynamic facade system for buildings which aims to maximize user comfort while simultaneously maximizing energy savings by harvesting solar energy optimally. The solar panels on the facade can harvest the highest amount of energy when it is positioned perpendicular to the sun’s rays. However, this may result in unsatisfactory lighting conditions inside the room and the problem is approached as a bi-objective optimization problem. This work is a preliminary exploration of the concept of smart skins for buildings that autonomously regulates light while harvesting solar energy, contributing to the creation of the future of sustainable buildings. The primary focus of this research work revolves around building a conceptual model, formulating an optimization problem, developing a control algorithm, iFOS, and then evaluating it. Data was simulated using advanced simulations to evaluate the dynamics of light indoors. Two benchmarks were created to evaluate the algorithm against, one where the system works towards maximizing user comfort indoors, and the other, where the system works to maximize the energy harvested by the facade. Upto 8% increase in the energy harvested was achieved with minimal loss in user comfort in the use case evaluated. The average energy figure for The Netherlands in the summer months is about 5 kW h/m2/day, which makes the total energy that can be captured at 20% efficiency to be about 750 kWh per day. The algorithm is found to work the best when the desired light level to be maintained indoors lies in the range [400,600] lux.

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Name: Luis A. Moreira Cardoso

Project description
Studies have shown that fresh water is getting scarcer worldwide day by day. Most of the freshwater is being wasted on land e.g. for irrigation. Farmers waste so much fresh water, when watering their crops. To conserve water, measurements need to be done efficiently. However, soil moisture sensors currently available on the market are quite expensive and have high power consumption to be used in developing countries extensively.

Currently, ICT technologies moving at a rapid pace has resulted in a broad domain called, Internet of Things (IoT), which is becoming more and more ubiquitous. IoT is being deployed as part of smart solutions for purposes as remote monitoring and automation, to solve individual as well as social needs. In this work we present a new solar-powered passive moisture sensor, that is a low-cost alternative moisture sensor with low power consumption. To reduce the power consumption we took two approaches. Firstly, we target to reduce the power consumption during operation and secondly, we aim to harvest solar energy to prevent battery depletion.
The Passive moisture sensor measures the volumetric water content in soil by means of two electrodes composed of different metals, such as copper and zinc. The potential difference between the metals changes with the change of moisture in the soil. Since, the measurements are based on the potential difference between the metals there is no energy consumed by the node. We show that measurements performed by the passive moisture sensor correlates with the measurements performed by commercial moisture sensors, such as, the Decagon EC5. Our passive moisture sensor solution is at least 20% less expensive than other solutions currently available on the market. To reduce the energy consumption as much as possible, we let both main components of the sensor node, microcontroller and radio, duty cycle separately, since each of these components has its own independent task. Therefore, we have developed the Harmonic-Medium Access Control (H-MAC) protocol, which ensures that the duty cycle of both components do not conflict with each other, and allows the communication between the sensor nodes and the Cloud to be bi-directional. H-MAC makes the network scalable and resilient to failure. With H-MAC the radio has lower duty cycle, lower energy consumption and has lower latency than X-MAC. Further, to ensure that the batteries of the nodes last for as long as possible we powered the nodes with solar energy. Since, solar energy is lacks consistency and reliability we need to predict when will the sun provide enough energy to power the sensor nodes. We show that by using the weather forecast and inserting information about solar radiation and sunshine duration into our prediction model we get an improvement of the error rate of about 10% on the prediction
of solar energy availability.

In this work we provide convincing results that our passive moisture sensor is a low cost, low power alternative for the market. Our solution is affordable for developing countries.

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Name: Antonio Reyes Lua

Project description
Providing detailed appliance level energy consumption may lead consumers to understand their usage behavior and encourage them to optimize the energy usage. Non-intrusive load monitoring (NILM) or energy disaggregation aims to estimate appliance level energy consumption from aggregate consumption data of households. Hitherto, proposed NILM algorithms are either centralized or require high performance systems to derive appliance level data, owing to the computational complexity associated. This approach raises several issues related to scalability and privacy of consumer’s data.

In this thesis, we present the NILM-Loc Framework that utilizes occupancy of users to derive accurate appliance level usage information. NILMLoc framework limits the appliances considered for disaggregation based on the current location of the occupants. Thus, it can provide real-time feedback on appliance level energy consumption and run on an embedded system locally at the household. We propose several accuracy metrics to study the performance of NILM-Loc. To test its robustness, we empirically evaluated it across multiple publicly available datasets. NILM-Loc has significantly higher energy disaggregation accuracy while exponentially reducing the computational complexity. NILM-Loc presents accuracy improvements up to 30% better than other traditional methods. It reaches an accuracy of 89% for the evaluated datasets.

We also detail a case study for the use of the fine-grained appliance-level energy information obtained. We present a load scheduler that minimizes cost and discomfort based on hourly day-ahead pricing. The proposed Demand Response (DR) system ensures user discomfort is minimized by abstracting patterns from past user behavior and incorporating them to the designed cost-optimal schedule.

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Name: Nikolaos Kouvelas

Project description
The advances of the information and communication technology (ICT) brought changes in the energy distribution domain, introducing the Smart Grid (SG). In SG, generators, distributors, and consumers communicate in a bidirectional way. SGs are envisaged to include micro-grids (MG) consisting of distributed control networks of consumers, producers, and the power grid. Two-way communication in MGs offers the opportunity to allocate the produced energy inside a community of consumers, and, as a result, make the energy flow less dependent on the central grid. However, challenges arise regarding energy sharing, namely: (i) how to balance the demand and supply inside communities; (ii) how to dictate the impact –for the community– of serving the needs of a household; and (iii) how to balance the economic benefit –under a policy– for everyone who participates. In this thesis, we propose energy allocation algorithms for MG communities consisting of households that use renewable sources of energy (RSEs). Our objective is to maximize the usage of the energy created by the producers inside the community and minimize the cost, under certain priority policies. Through an in-depth analysis of energy and socioeconomic data of the community, we form groups of households that share similar characteristics. Since these groups share similar energy trends, we can decide the (group of) consumers that should be served first or that should accept higher amounts of energy than the rest, by dictating consumer priority policies (CPPs). Then, after defining the value of serving each consumer inside the community (by imposing a CPP), we create energy allocation strategies (EASs). These are algorithms which define the way in which the produced energy will be distributed, based on the already imposed CPP. We present seven, simple and optimized, EASs and several consumer priority policies (CPPs). Our EASs and CPPs are scalable and can meet the specific needs of an MG community. We evaluate our algorithms and techniques using real data, acquired from a community of 443 households over a year. We show that the groups of households that we prioritize cover their needs of energy, sometimes completely, in periods of high energy production. We compare the cost of trading energy within the MG and requesting energy from the grid (classic way). The expenses for prioritized groups of consumers under our EASs are decreased, up to 50% in certain cases. Further, it is shown that even the non-prioritized consumers benefit economically by allocating the excess of energy.

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Name: Noortje Aalbers

Project description
The aim of the research presented in this thesis is to design the characteristics of an Adaptive Thermal Comfort System for Dwellings to achieve a significantly better energy performance whilst not compromising the thermal comfort perception of the occupants. An Adaptive Thermal Comfort System is defined as the whole of passive and active comfort components of the dwelling that dynamically adapts its settings to varying user comfort demands and weather conditions (seasonal, diurnal and hourly depending on the aspects adapted), thus providing comfort only where, when and at the level needed by the user, to improve possibilities of harvesting the environmental energy (e.g. solar gain and outdoor air) when available and storing it when abundant. In order to be able to create an Adaptive Thermal Comfort System to save energy knowledge is needed as to where, when, what kind and how much energy is needed to provide the thermal comfort. Therefore, this research aimed to gain insight in the dynamic behavior of the weather and the occupant and the opportunities to design the characteristics of an Adaptive Thermal Comfort System for Dwellings to achieve a significantly better energy performance whilst not compromising the thermal comfort perception of the occupants answering the main research question; What are the most efficient strategies for delivering thermal comfort in the residential sector with respect to better energy performances and an increasing demand for flexibility in use and comfort conditions?

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Name: C. Sarkar

Project description
Computers were invented to automate the labour-intensive computing process. The advancement of semiconductor technology has reduced the form-factor and cost of computers, and increased their usability. This has gradually introduced computers in various control and automation systems. The further rise of miniatuarized computing devices paves the way for autonomous monitoring using embedded devices. In the last two decades, we observed a huge surge of such monitoring and control systems. These systems are generally termed as the wireless sensor and actuator networks (WSAN). In a WSAN, a number of sensor nodes monitors a deployment area where data collection by humans is either difficult or costly. These devices collaboratively report their sensor readings to a centralized node called the sink. The sink is connected with the Internet, thus delivers the data to the outside world. This way the deployment region can be monitored remotely. Similarly, some actuators can also be controlled remotely through the sink.In the last decade, the concept of the Internet of Things (IoT) has evolved where any device can be reached by any other device/system/human being from anywhere and anytime. Thus, WSANs can be seen as a precursor of IoT. However, the vision of IoT is not limited to mere remote connectivity. Unlike traditional WSAN, where devices are deployed in remote/critical locations for specific purposes, IoT devices would be integrated into our daily surroundings assisting us in every aspect of life. As the embedded devices are resource constrained, energy and computational efficiency is a major challenge for both WSAN and IoT devices. However, the problem escalates as the IoT devices are expected to perform a number of tasks as opposed to a specific task as performed by classical WSANs. Moreover, the goal of IoT is to take humans out of the control loop or reduce the human intervention as much as possible. This requires devices to exchange data and cooperate among themselves. Thus, IoT devices need to act smartly fulfilling various requirements within its resource constraints. Every existing and upcoming device and network would be part of the IoT ecosystem. As the number of devices is expected to grow multifold, managing these devices will be a challenge. Especially since these devices are under the control of various entities/organizations. Not to mention that the manufacturers of various devices and their specifications would also vary significantly. To accomplish the vision of IoT these devices need to be able to cooperate and collaborate among themselves even if they are managed differently. This thesis brings forward the concept of virtualization in IoT to tackle the challenges of a global IoT ecosystem. The first challenge that we tackle is how to virtualize the IoT. We propose a reference architectural model for IoT called DIAT. The reference architecture follows a layered design principle where each layer groups a number of similar functionalities together. This enables easy development of existing and new functionalities of each layer independently. To validate the feasibility and usability of such an architectural model, we developed a system based on a practical IoT-application scenario. To this extent, we developed a controller (iLTC) that operates the heating and lighting systems in an office environment such that these devices operate energy efficiently. At the same time the system ensures a comfortable surroundings for the occupants while eliminating any direct involvement from the occupants. As WSANs are an integral part of the IoT ecosystem, next, we revisited some of the classic problems of WSANs in the wake of virtualizing the IoT. As energy efficiency is one of the biggest issues in WSANs, we propose a solution to reduce the overall traffic in a network without affecting the quality of data/monitoring. We achieved this by virtualizing the WSAN, which leads to higher cooperation among the devices and a higher operational optimization. We developed the virtual sensing framework (VSF) that exploits the inherent correlation among the sensor nodes to predict sensor readings (virtual sensing). The basic idea is that if a number of nodes are highly correlated, sensor readings from only one of them is sufficient to predict the readings for rest of them. Due to virtualization, such a cooperation among the nodes is possible. This reduces the amount of data transfer within the network, which leads to energy-efficient network operation. Further, we developed an efficient data collection protocol, called Sleeping Beauty that complements the virtualized sensor network. Based on a centralized schedule, nodes deliver their sensor readings to the sink reliably and efficiently. The accomplishment of a centralized schedule depends on network-wide time synchronization. As the hardware clock of an embedded device drifts significantly within a short time span, we developed a simple self-rectification mechanism such that the overhead of synchronizing the network periodically can be reduced significantly. This technique can be used by any protocol that requires time synchronization other than Sleeping Beauty. Timely data collection is another desired aspect of IoT as opposed to classical WSANs where latency is generally compromised in order to achieve a higher energy efficiency. We developed a communication mechanism, called Rapid that not only delivers the sensor readings in a fixed time bound, it also reduces the energy consumption. Rapid forms a number of clusters on-the-fly, where the cluster-heads collect data from the cluster-members and send an aggregated packet to the sink. By exploiting the capture effect, Rapid achieves parallelization for intra-cluster communications. Further, it exploits the constructive interference based fast flooding to deliver the aggregated data, which eliminates hop-by-hop flow scheduling. These two factors reduces the overall end-to-end delay of all the flows. The proposition of this thesis is that by means of virtualization, traditional WSANs can be easily integrated into the grand vision of IoT. We proposed a reference architecture, validated by means of a case study, and developed several amendments to classical WSAN data collection, making it consume less energy and achieve lower latency. We are convinced that virtualization can be applied effectively to other (WSAN) functionality as well. The future of IoT is looking bright.

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Name: A.U.N. Srirangam Narashiman

Project description
The rapid pace of urbanization has an impact on climate change and other environmental issues. Currently, 54% of the global population lives in cities accounting for two-thirds of global energy demand. Sustainable energy generation and consumption is the top humanity’s problem for the next 50 years. Faced with rising urban population and the need to achieve energy efficiency, urban planners are focusing on sustainable, smart energy systems. This has led to the development of Smart Grids (SG) that employs intelligent monitoring, control and communication technologies to enhance efficiency, reliability and sustainability of power generation and distribution networks. While energy utilities are optimizing energy generation and distribution, consumers play a key role in sustainable energy usage. Several energy services are provided to the consumers to know households' hourly energy consumption, estimate monthly electricity cost and recommendations to reduce energy consumption. Furthermore, advanced services such as demand response, can now control and influence energy demand at the consumer-end to reduce the overall peak demand and re-shape demand profiles. The effectiveness and adoption of these services highly depend on the consumers’ awareness, their participation and engagement. Current energy services seldomly consider consumer preferences such as their daily behavior, comfort level and energy-consumption pattern. In this thesis, we investigate development of personalized energy services that strive to achieve a balance between efficient-energy consumption and user comfort. Personalization refers to tailoring energy services based on individual consumers’ characteristics, preferences and behavior. To develop effective personalized energy services a set of challenges need to be tackled. First, fine-grained data collection at user and appliance level is required (data collection challenge). Mechanisms should be devised to collect fine-grained data at various levels in a non-intrusive way with minimal sensors. Second, personalized energy services require detailed user preferences such as their thermal comfort level, appliance usage behavior and daily habits (user preference challenge). Accurate learning models to derive user preferences with minimal training and intrusion are required. Third, energy services developed needs to be easily scalable, from one household to tens and thousands of households (scalability challenge). Mechanisms should be developed to tackle the deluge of data and support distributed storage and processing. Fourth, energy services should deliver real-time feedback or recommendations so that users can promptly act upon it (real time challenge). This calls for development of distributed and low complexity algorithms. This thesis moves away from traditional SG services -- which hardly consider consumer preferences and comfort -- and proposes a novel approach to develop effective personalized energy services. The proposed energy services provide actionable feedback, raise awareness and promote energy-saving behavior among consumers. In this thesis, we follow a bottom-up data-driven methodology to develop personalized energy services at various scales -- (i) nano: individual households, (ii) micro: buildings and spaces, and (iii) macro: neighborhoods and cities. To this end, we present our approach -- physical analytics for sustainable, smart energy systems -- that combines IoT data, physical modeling and data analytics to develop intelligent, personalized energy services. Physical analytics fuses data from various Internet of Things (IoT) devices such as smart meters, smart phones and smart watches, along with physical information such as household type, demographics and occupancy to infer energy-usage patterns, user behavior and discover hidden patterns. This approach is used to learn and model user preferences and energy usage, subsequently, employed to develop personalized energy services. This thesis is organized into three parts. Part I describes how to derive fine-grained information with minimal sensors and intrusion. We present two novel algorithms viz., LocED and PEAT that derive fine-grained information from appliance and user level, respectively. This real-time information is used to raise awareness on energy-usage behavior among occupants. Part II presents personalized energy services targeted at households and buildings. We develop services that shift and/or reduce energy consumption and cost by considering individual consumers’ preferences and comfort. These energy services are aimed at providing actionable feedback to occupants towards sustainable energy usage. Part III presents energy services targeted at neighborhood and city level. These energy services aim to identify target consumers in a neighborhood based on their energy-usage pattern and preferences for various DR programs. Finally, we present data-processing architectures that investigate how to cope with the overwhelming data generated from smart meters towards design and development of sustainable, smart energy systems. This thesis advocates that the design and development of energy services should follow personalized approach with consumer preferences and comfort given paramount importance. Results show that the personalized energy services developed has significant potential to raise awareness, reduce energy consumption and improve user comfort in smart -- homes, buildings and neighborhoods.

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