Depletion of fossil fuel and the ever-increasing need for energy in residential and commercial buildings have triggered in-depth research on many energy saving and energy monitoring mechanisms. Currently, users are only aware of their overall energy consumption and its cost in a shared space. Due to the lack of information on individual energy consumption, users are not being able to fine-tune their energy usage. Further, even-splitting of energy cost in shared spaces does not help in creating awareness. With the advent of the Internet of Things (IoT) and wearable devices, apportioning of the total energy consumption of a household to individual occupants can be achieved to create awareness and consequently promoting sustainable energy usage. However, providing personalized energy consumption information in real-time is a challenging task due to the need for collection of fine-grained information at various levels. Particularly, identifying the user(s) utilizing an appliance in a shared space is a hard problem. The reason being, there are no comprehensive means of collecting accurate personalized energy consumption information. In this paper we present the Personalized Energy Apportioning Toolkit (PEAT) to accurately apportion total energy consumption to individual occupants in shared spaces. Apart from performing energy disaggregation, PEAT combines data from IoT devices such as smartphones and smartwatches of occupants to obtain fine-grained information, such as their location and activities. PEAT estimates energy footprint of individuals by modeling the association between the appliances and occupants in the household. We propose several accuracy metrics to study the performance of our toolkit. PEAT was exhaustively evaluated and validated in two multi-occupant households. PEAT achieves 90% energy apportioning accuracy using only the location information of the occupants. Furthermore, the energy apportioning accuracy is around 95% when both location and activity information is available.

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Smart grids offer better energy management for consumers as well as energy companies using bi-directional communication and control. With the advent of smart homes, energy companies can balance energy supply and demand to a large extent using many sensors/meters deployed. They can also nudge consumers to shift their demands to off-peak hours for load balancing and monetary benefits. We propose a decentralized demand scheduling algorithm that minimizes consumer discomfort and electricity cost of a household. Our algorithm utilizes only aggregated energy consumption of a household to derive optimal appliance level demand schedules. Furthermore, a low-complexity energy disaggregation algorithm is proposed to derive fine-grained appliance information and consumer preferences. We propose three important coefficients related to the energy usage of consumers. We utilize them to derive optimal day-ahead demand schedules. The decentralized algorithm is empirically evaluated using real-world energy usage data from open datasets, which include our own deployment. Our proposed scheduling algorithm saves up to 30% energy cost. This paper is one of the first to derive day-ahead schedules using real-world data from multiple households.@en

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. @en

Smart grids offer better energy management at consumer premises as well as energy companies side using bi- directional communication and control. Energy companies can balance energy supply and demand to a large extent, with the advent of smart homes. They can also nudge consumers to shift their demands to off-peak hours for load balancing and monetary benefits. We propose a decentralized demand scheduling algorithm that minimizes consumer discomfort and electricity cost of a household. Our algorithm utilizes only aggregated energy consumption of a household to derive optimal appliance level demand schedules. Furthermore, a low-complexity energy disaggregation algorithm is proposed to derive fine- grained appliance information and consumer preferences. We propose three important coefficients related to energy usage of consumers. We utilize them to derive optimal day- ahead demand schedules. The decentralized algorithm is empirically evaluated using real-world energy usage data from open datasets, which include our own deployment. Our proposed scheduling algorithm saves up to 30% energy cost. This work is one of the first to derive day-ahead schedules using real-world data from multiple households.@en

Advancement in communication and computing technology is driving the next-generation electrical smart grid, SG. SG envisions developing user-centric distributed systems to offer cost-effective and reliable power supply.
Its effectiveness depends highly on consumer awareness and engagement. SG deployments and programs have been found to be lacking in active consumer participation. We address this by proposing a techno-social framework for SG,
TSSG, wherein technologies related to energy infrastructure interface with social activities of consumers. Social interactions play a crucial role in preferences of consumers and the decisions they make. The rapid evolution of social networks now enables us to model and capture these interactions. The proposed framework combines social networks with energy networks to understand individual and collective behavior of consumers in order to change the energy
demand patterns. We describe several mechanisms to enable harnessing useful data from such a framework, including its applicability to various SG applications. Specifically, we illustrate the benefits of collective modeling of techno-social aspects by developing goal-oriented virtual communities. This is one of the first articles to consider both energy consumption information and characteristics of consumers to determine such communities. We employed data from a real-world SG deployment with more than 4000 households along with their preferences, opinions, and interests to evaluate our proposal.@en

The increase in the deployment of smart meters has enabled collection of fine-grained energy consumption data at consumer premises. Analysis of this real-time energy consumption data bestows new opportunities for better demand–response (DR) programs. This paper offers a new perspective to study energy demand and helps in designing novel mechanisms for decentralized demand-side management. Specifically, a new concept of finding the demand states using energy consumption of consumers over time and feasible transitions therein is introduced. It is shown that the orchestration of temporal transitions between the demand states can meet broad range of smart grid objectives. An online demand regulation model is developed that captures the temporal dynamics of energy demand to identify target consumers for different DR programs. This methodology is empirically evaluated and validated
using data from more than 4000 households, which were part of a real-world smart grid project. This paper is the first one to comprehensively analyze the temporal dynamics of demands. @en

Providing detailed appliance level energy consumption information 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 the aggregate consumption data of households. NILM algorithms, proposed hitherto, are either centralized or do 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 paper, we present the Location-aware Energy Disaggregation Framework (LocED) that utilizes occupancy of users to derive accurate appliance level usage information. LocED framework limits the appliances considered for disaggregation based on the current location of occupants. Thus, LocED 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 LocED. To test the robustness of LocED, we empirically evaluated it across multiple publicly available datasets. LocED has significantly high energy disaggregation accuracy while exponentially reducing the computational complexity. We also release our comprehensive dataset DRED (Dutch Residential Energy Dataset) for public use, which measures electricity, occupancy and ambient parameters of the household.@en

The advent of Internet of Things (IoT) has boosted the growth in number of devices around us and kindled the possibility of umpteen number of applications. One of the major challenges in the realization of IoT applications is interoperability among various IoT devices and deployments. Thus, the need for a new architecture-comprising smart control and actuation-has been identified by many researchers. In this paper, we propose a Distributed Internet-like Architecture for Things (DIAT), which will overcome most of the obstacles in the process of large-scale expansion of IoT. It specifically addresses heterogeneity of IoT devices, and enables seamless addition of new devices across applications. In addition, we propose an usage control policy model to support security and privacy in a distributed environment. We propose a layered architecture that provides various levels of abstraction to tackle the issues such as scalability, heterogeneity, security, and interoperability. The proposed architecture is coupled with cognitive capabilities that helps in intelligent decision-making and enables automated service creation. Using a comprehensive use-case, comprising elements from multiple-application domains, we illustrate the usability of the proposed architecture.@en

Automatic control of HVAC and artificial lights has been one of the popular methods for achieving energy-efficient buildings. The operating set-points are decided based on predefined values to ensure comfort level to most of the occupants based on prior studies. However, a person can feel comfortable beyond the traditional set-point ranges used in the energy management systems of buildings. In this work, we develop a smartphone application that learns individual preferences about thermal and visual comfort with minimal user intervention. These functions provide the flexibility to operate the controllers in an aggressively lower energy consuming state while maintaining the comfort level of the occupants. Using a HVAC energy consumption model, we show that individual comfort preference based set-point can attain lesser energy consumption as compared to fixed set-point.@en