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Analysis Modeling And Optimization Of Residential Energy Use From Smart Meter Data
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Book Synopsis Analysis, Modeling and Optimization of Residential Energy Use from Smart Meter Data by : Krystian Xavier Perez
Download or read book Analysis, Modeling and Optimization of Residential Energy Use from Smart Meter Data written by Krystian Xavier Perez and published by . This book was released on 2016 with total page 352 pages. Available in PDF, EPUB and Kindle. Book excerpt: Approximately 38% of electricity consumption within the United States can be attributed to residential buildings, a vast share of which is in heating, ventilation and cooling. The load placed on the grid by residential consumers is highly variable and strongly influenced by weather and human activity patterns. Meeting fluctuations in demand is challenging and expensive for electricity producers and grid operators. Reducing variability in residential energy use can contribute significantly to increasing the uniformity of energy demand on the grid and diminish reliance on inefficient, polluting “peaking” plants that are used to meet extremely high demands. Achieving this goal requires tight coordination between energy consumption and generation, as well as the means to store energy generated in periods of low demand for use during the time intervals when consumer demand peaks. There is a common perception that a single home has a minor impact on the entire grid. However, owing to the fact that consumption patterns of homes are similar, while a single home does not have a large impact on the grid, entire neighborhoods do. Motivated by the above, this work explores the interaction between residential energy consumption and the electric grid. An analysis, modeling and optimization framework on smart meter data is developed to anticipate and modulate energy usage of ensembles of residential homes in order to reduce peak power demand. Much of the data used in this work come from Pecan Street, Inc., a smart grid demonstration project in Austin, TX. First, a nonintrusive load monitoring algorithm is developed to isolate air-conditioning (A/C) energy use from whole-house energy consumption data. Subsequently, a simplified reduced-order model is derived from smart meter data and thermostat set-point data to predict A/C energy use. The models of an ensemble of homes are placed within a centralized model predictive control scheme to minimize peak community A/C energy use. Reductions in peak energy use are achieved by shifting the thermostat set-points of individual homes. The approach is further expanded by simultaneously scheduling the operation of time-shiftable appliances to further reduce the community peak load. This integrated operation reduces peak loads by an average of 25.5%. This work also considers the impact of control and optimization techniques on designing a micro-grid that operates near autonomously from the electric power grid. Lastly, this work presents a tool to compare energy demand patterns of houses from smart meter data and indicates that high-energy houses would benefit from energy audits to improve energy efficiency.
Book Synopsis Scalable Data-driven Modeling and Analytics for Smart Buildings by : Srinivasan Iyengar
Download or read book Scalable Data-driven Modeling and Analytics for Smart Buildings written by Srinivasan Iyengar and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Buildings account for over 40% of the energy and 75% of the electricity usage. Thus, by reducing our energy footprint in buildings, we can improve our overall energysustainability. Further, the proliferation of networked sensors and IoT devices in recent years have enabled monitoring of buildings to provide data at various granularity. For example, smart plugs monitor appliance level usage inside the house, while solar meters monitor residential rooftop solar installations. Furthermore, smart meters record energy usage at a grid-scale. In this thesis, I argue that data-driven modeling applied to the IoT data from a smart building, at varying granularity, in association with third party data can help to understand and reduce human energy consumption. I present four data-driven modeling approaches - that use sophisticated techniques from Machine Learning, Optimization, and Time Series Analysis - applied at different granularities. First, I study IoT devices inside the house and discuss an approach called NIMD that automatically models individual electrical loads found in a household. The analytical model resulting from this approach can be used in several applications. For example, these models can improve the performance of NILM algorithms to disaggregate loads in a given household. Further, faulty or energy-inefficient appliances can be identified by observing deviations in model parameters over its lifetime. Second, I examine data from solar meters and present a machine learning framework called SolarCast to forecast energy generation from residential rooftop installations. The predictions enable exploiting the benefits of locally-generated solar energy. Third, I employ a sensorless approach utilizing a graphical model representation to report city-scale photovoltaic panel health and identify anomalies in solar energy production. Immediate identification of faults maximizes the solar investment by aiding in optimal operational performance. Finally, I focus on grid-level smart meter data and use correlations between energy usage and external weather to derive probabilistic estimates of energy, which is leveraged to identify the least efficient buildings from a large population along with the underlying cause of energy inefficiency. The identified homes can be targeted for custom energy efficiency programs.
Book Synopsis Smart Meter Data Analytics by : Yi Wang
Download or read book Smart Meter Data Analytics written by Yi Wang and published by Springer Nature. This book was released on 2020-02-24 with total page 306 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book aims to make the best use of fine-grained smart meter data to process and translate them into actual information and incorporated into consumer behavior modeling and distribution system operations. It begins with an overview of recent developments in smart meter data analytics. Since data management is the basis of further smart meter data analytics and its applications, three issues on data management, i.e., data compression, anomaly detection, and data generation, are subsequently studied. The following works try to model complex consumer behavior. Specific works include load profiling, pattern recognition, personalized price design, socio-demographic information identification, and household behavior coding. On this basis, the book extends consumer behavior in spatial and temporal scale. Works such as consumer aggregation, individual load forecasting, and aggregated load forecasting are introduced. We hope this book can inspire readers to define new problems, apply novel methods, and obtain interesting results with massive smart meter data or even other monitoring data in the power systems.
Book Synopsis Data-driven Approaches to Load Modeling Andmonitoring in Smart Energy Systems by : Guoming Tang
Download or read book Data-driven Approaches to Load Modeling Andmonitoring in Smart Energy Systems written by Guoming Tang and published by . This book was released on 2017 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In smart energy systems, load curve refers to the time series reported by smart meters, which indicate the energy consumption of customers over a certain period of time. The widespread use of load curve (data) in demand side management and demand response programs makes it one of the most important resources. To capture the load behavior or energy consumption patterns, load curve modeling is widely applied to help the utilities and residents make better plans and decisions. In this dissertation, with the help of load curve modeling, we focus on data-driven solutions to three load monitoring problems in different scenarios of smart energy systems, including residential power systems and datacenter power systems and covering the research fields of i) data cleansing, ii) energy disaggregation, and iii) fine-grained power monitoring. First, to improve the data quality for load curve modeling on the supply side, we challenge the regression-based approaches as an efficient way to load curve data cleansing and propose a new approach to analyzing and organizing load curve data. Our approach adopts a new view, termed portrait, on the load curve data by analyzing the inherent periodic patterns and re-organizing the data for ease of analysis. Furthermore, we introduce strategies to build virtual portrait datasets and demonstrate how this technique can be used for outlier detection in load curve. To identify the corrupted load curve data, we propose an appliance-driven approach that particularly takes advantage of information available on the demand side. It identifies corrupted data from the smart meter readings by solving a carefully-designed optimization problem. To solve the problem efficiently, we further develop a sequential local optimization algorithm that tackles the original NP-hard problem by solving an approximate problem in polynomial time. Second, to separate the aggregated energy consumption of a residential house into that of individual appliances, we propose a practical and universal energy disaggregation solution, only referring to the readily available information of appliances. Based on the sparsity of appliances' switching events, we first build a sparse switching event recovering (SSER) model. Then, by making use of the active epochs of switching events, we develop an efficient parallel local optimization algorithm to solve our model and obtain individual appliances' energy consumption. To explore the benefit of introducing low-cost energy meters for energy disaggregation, we propose a semi-intrusive appliance load monitoring (SIALM) approach for large-scale appliances situation. Instead of using only one meter, multiple meters are distributed in the power network to collect the aggregated load data from sub-groups of appliances. The proposed SSER model and parallel optimization algorithm are used for energy disaggregation within each sub-group of appliances. We further provide the sufficient conditions for unambiguous state recovery of multiple appliances, under which a minimum number of meters is obtained via a greedy clique-covering algorithm.Third, to achieve fine-grained power monitoring at server level in legacy datacenters, we present a zero-cost, purely software-based solution. With our solution, no power monitoring hardware is needed any more, leading to much reduced operating cost and hardware complexity. In detail, we establish power mapping functions (PMFs) between the states of servers and their power consumption, and infer the power consumption of each server with the aggregated power of the entire datacenter. We implement and evaluate our solution over a real-world datacenter with 326 servers. The results show that our solution can provide high precision power estimation at both the rack level and the server level. In specific, with PMFs including only two nonlinear terms, our power estimation i) at the rack level has mean relative error of 2.18%, and ii) at the server level has mean relative errors of 9.61% and 7.53% corresponding to the idle and peak power, respectively.
Book Synopsis Model-driven Analytics of Energy Meter Data in Smart Homes by : Sean Barker
Download or read book Model-driven Analytics of Energy Meter Data in Smart Homes written by Sean Barker and published by . This book was released on 2014 with total page 110 pages. Available in PDF, EPUB and Kindle. Book excerpt: The proliferation of smart meter deployments has led to significant interest in analyzing home energy use as part of the emerging 'smart grid'. As buildings account for nearly 40% of society's energy use, data from smart meters provides significant opportunities for both utilities and consumers to optimize energy use, minimize waste, and provide insight into how modern homes and devices use energy. Meter data is often difficult to analyze, however, owing to the aggregation of many disparate and complex loads as well as relatively coarse measurement granularities. At utility scales, analysis is further complicated by the vast quantity of data, which precludes the use of computationally intensive techniques when monitoring hundreds or even thousands of homes. In this thesis, I present an architecture for enabling smart homes using smart energy meters, encompassing efficient data collection and analysis to understand the behavior of home devices. I consider four primary challenges within this domain: (1) providing low-overhead data collection and processing for many devices, (2) designing models characterizing the energy use of modern devices, (3) using these models to track the real-time behavior of known devices, and (4) automatic identification of unknown devices in the home. To enable practical smart homes, my proposed architecture combines low-cost, off-the-shelf sensing equipment with a hybrid local and cloud-based processing backend. To analyze data within the environment, I first characterize the basic device types present in today's homes (e.g., resistive, inductive, or non-linear) and distill the essential usage characteristics of each type. Using these characteristics, I construct a set of models that more accurately represents real-world devices than previous simplistic models. I then leverage this modeling framework to track the behavior of specific devices, using a technique that runs in close to real-time and can scale to many devices. Finally, I present a technique to automatically identify unknown devices attached to smart outlets in homes, which relieves homeowners of the need to manually describe devices in order to employ smart home optimizations.
Book Synopsis Targeted Efficiency by : Samuel Dalton Borgeson
Download or read book Targeted Efficiency written by Samuel Dalton Borgeson and published by . This book was released on 2013 with total page 206 pages. Available in PDF, EPUB and Kindle. Book excerpt: Energy efficiency (EE) and demand response (DR) programs are designed to reduce energy consumption, mitigate grid capacity constraints, support intermittent renewable energy integration, and reduce pollution for less than the cost of additional generation. However, the savings and flexibility achieved by EE and DR programs are contingent on programs finding and enrolling customers well matched to program objectives. Many EE programs currently rely on broad but shallow savings from prescriptive measures, but growing interest in deeper and more reliable savings is leading to increased attention for program planning and targeting using empirical criteria. Through data gathered by smart meters and software designed to manage and analyze large data sets, the tools required to cost effectively characterize, target, and change patterns of energy demand are beginning to emerge. This dissertation consists of three chapters on the analysis of meter data and one on the policy implications of these new capabilities. Chapter 2 analyzes hourly electricity and daily natural gas smart meter readings from 30,000 residential customers of Pacific Gas and Electric (PG & E). Meter data is used to derive distributions of previously unobserved characteristics of the housing stock. We show that the targeting of EE and DR programs would be improved through selection of households based on their positions within these distributions. It follows that every utility (or public utility commission) with sufficient metering infrastructure could apply similar techniques to improve the targeting, implementation, and evaluation of their energy efficiency and demand response programs. Chapter 3 uses regression models of patterns in daily household electricity consumption to estimate the physical and operational characteristics of homes. We apply semi-physical regressors designed to capture patterns in the space-heating and cooling, scheduling, and occupancy of homes. When applied to data from approximately 160,000 PG & E customers, this approach supports an evaluation of competing regression model formulations and provides distributions of model coefficients used to evaluate patterns of domestic energy use, including annual and system peak coincident air conditioning loads, cooling set points, day of week scheduling, and lighting energy. Chapter 4 presents a method for estimating the hourly timing of occupant driven loads based on smart meter data. The residuals of a predictive regression model are assumed to include occupant activities because occupant controlled energy use is not fully determined by externally observable factors. Occupant activity timing is converted into empirical distributions of the probability of such events by hour-of-day or day-of-week. With estimates calculated for approximately 25,000 PG & E customers and grouped using K-means clustering, prevailing patterns are interpreted as the result of occupant lifestyles -- with applications in efficiency and demand response program targeting. Drawing upon the applications developed in the preceding chapters, Chapter 5 discusses the potential for using smart meter data to support public interest utility programs in the context of ongoing concerns over public disclosure and privacy concerns, including malicious use by bad actors and inappropriate commercial use. We propose differentiated levels of access to meter data, with access to data mediated by delegated analysis, which allows stakeholders to receive the outputs of approved algorithms without requiring direct access to sensitive data. Such a system would provide privacy protection and oversight without foreclosing on creative and innovative uses of meter data.
Book Synopsis Leveraging Smart Meter Data Through Advanced Analytics by : Saurabh Jalori
Download or read book Leveraging Smart Meter Data Through Advanced Analytics written by Saurabh Jalori and published by . This book was released on 2013 with total page 159 pages. Available in PDF, EPUB and Kindle. Book excerpt: The poor energy efficiency of buildings is a major barrier to alleviating the energy dilemma. Historically, monthly utility billing data was widely available and analytical methods for identifying building energy efficiency improvements, performing building Monitoring and Verification (M & V) and continuous commissioning (CCx) were based on them. Although robust, these methods were not sensitive enough to detect a number of common causes for increased energy use. In recent years, prevalence of short-term building energy consumption data, also known as Energy Interval Data (EID), made available through the Smart Meters, along with data mining techniques presents the potential of knowledge discovery inherent in this data. This allows more sophisticated analytical tools to be developed resulting in greater sensitivities due to higher prediction accuracies; leading to deep energy savings and highly efficient building system operations. The research explores enhancements to Inverse Statistical Modeling techniques due to the availability of EID. Inverse statistical modeling is the process of identification of prediction model structure and estimates of model parameters. The methodology is based on several common statistical and data mining techniques: cluster analysis for day typing, outlier detection and removal, and generation of building scheduling. Inverse methods are simpler to develop and require fewer inputs for model identification. They can model changes in energy consumption based on changes in climatic variables and up to a certain extent, occupancy. This makes them easy-to-use and appealing to building managers for evaluating any general retrofits, building condition monitoring, continuous commissioning and short-term load forecasting (STLF). After evaluating several model structures, an elegant model form was derived which can be used to model daily energy consumption; which can be extended to model energy consumption for any specific hour by adding corrective terms. Additionally, adding AR terms to this model makes it usable for STLF. Two different buildings, one synthetic (ASHRAE medium-office prototype) building and another, an actual office building, were modeled using these techniques. The methodologies proposed have several novel features compared to the manner in which these models have been described earlier. Finally, this thesis investigates characteristic fault signature identification from detailed simulation models and subsequent inverse analysis.
Book Synopsis Towards Energy Smart Homes by : Stephane Ploix
Download or read book Towards Energy Smart Homes written by Stephane Ploix and published by Springer Nature. This book was released on 2021-11-11 with total page 627 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book exemplifies how smart buildings have a crucial role to play for the future of energy. The book investigates what already exists in regards to technologies, approaches and solutions both with a scientific and technological point of view. The authors cover solutions for mirroring and tracing human activities, optimal strategies to configure home settings, and generating explanations and persuasive dashboards to get occupants better committed in their home energy managements. Solutions are adapted from the fields of Internet of Things, physical modeling, optimization, machine learning and applied artificial intelligence. Practical applications are given throughout.
Book Synopsis Baseline Energy Modeling Approach for Residential Measurement and Verification Applications by : Eliot Crowe
Download or read book Baseline Energy Modeling Approach for Residential Measurement and Verification Applications written by Eliot Crowe and published by . This book was released on 2015 with total page 48 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The growing availability of electric interval data from smart meters is driving development of consumer-facing analytical software that has the potential to offer automated measurement and verification (M&V) of savings from energy efficiency projects. This capability would present new opportunities to utilities including supporting financial transactions based on measured energy savings, or allowing for a greater variety of program measures. This report provides the results of analytical research on establishing a method for developing a robust energy baseline regression model and evaluation of how the model performed on the electric interval dataset from NEEA's Residential Building Stock Analysis metering study. The report presents results of a literature review and a high-level review of home energy management system (HEMS) M&V capabilities. The results of the analytical research provide a strong foundation for future efforts toward an automated M&V approach using interval data, while the literature review confirmed the team's initial belief that utilities have not yet applied M&V approaches for residential applications using interval data. Additionally, the HEMS research uncovered no instances of products performing utility program M&V"--Publisher's description (viewed Sept. 4, 2015).
Book Synopsis Generating Insights from Smart Meter Data by : Anastasia Ushakova
Download or read book Generating Insights from Smart Meter Data written by Anastasia Ushakova and published by . This book was released on 2019 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: The introduction of smart meter technology has been central to recent innovations in energy provision for the UK residential sector. Smart meters have the potential to give greater insight into energy consumption behaviour for energy providers and researchers alike. For example, they may aid our understanding of how the consumption of gas and electricity may be replaced by the energy from renewable sources, or how consumer behaviours can be changed to reduce overall energy consumption, increase efficiency, and lessen the pressure on the national grid networks. The advantage of a thorough understanding of the insights generated from smart meter data for policy issues may sound obvious at a first glance. However, there are significant challenges associated with the availability of methods and computation necessary to perform a complete analysis of the available data. The thesis provides an in depth look at the nature of energy consumption through an analysis of big data that is recorded by around 400,000 smart meters installed at residential properties across the UK. It further discusses how this data is different from perhaps more conventionally collected retail consumer data, and in what way does the temporal nature of these data reveal information about the customers dynamics without compromising their anonymity. Various machine learning methods are applied and surveyed against more conventional methods often used by researchers and industry practitioners. Some extensions to improve the accuracy and reliability of methods for both segmentation of the behaviour, and prediction are also suggested. Lastly, a case study looking at identifying the fuel poor from smart meter data is presented as an illustrative example of potential research questions one may answer with smart meter data records.
Book Synopsis Smart Metering Applications by : Nikolaos Efkarpidis
Download or read book Smart Metering Applications written by Nikolaos Efkarpidis and published by Springer Nature. This book was released on 2022-10-03 with total page 147 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents a large number of smart metering applications from the points of view of different stakeholders. The applications are clustered with respect to three types of stakeholders: (a) end-customers, (b) energy service providers, and (c) authorities/research institutions or other organizations. The goal of the book is to examine the implementation potential for each application, considering the interests and benefits for the key stakeholders, main technical and regulatory requirements, as well as limitations and barriers. A business case for each application is created that can provide guidelines to the stakeholders involved in its realization. The book additionally investigates current business models for smart metering applications. A survey on the current techno-economic potential of such applications is conducted based on a questionnaire filled by various stakeholders. The book will be of interest to academic/research institutions, but also engineers in industry, authorities or other organizations.
Book Synopsis Data-driven Modeling and Optimization of Building Energy Consumption by : Divas Grover
Download or read book Data-driven Modeling and Optimization of Building Energy Consumption written by Divas Grover and published by . This book was released on 2019 with total page 56 pages. Available in PDF, EPUB and Kindle. Book excerpt: Sustainability and reducing energy consumption are targets for building operations. The installation of smart sensors and Building Automation Systems (BAS) makes it possible to study facility operations under different circumstances. These technologies generate large amounts of data. That data can be scrapped and used for the analysis. In this thesis, we focus on the process of data-driven modeling and decision making from scraping the data to simulate the building and optimizing the operation. The City of Orlando has similar goals of sustainability and reduction of energy consumption so, they provided us access to their BAS for the data and study the operation of its facilities. The data scraped from the City's BAS serves can be used to develop statistical/machine learning methods for decision making. We selected a mid-size pilot building to apply these techniques. The process begins with the collection of data from BAS. An Application Programming Interface (API) is developed to login to the servers and scrape data for all data points and store it on the local machine. Then data is cleaned to analyze and model. The dataset contains various data points ranging from indoor and outdoor temperature to fan speed inside the Air Handling Unit (AHU) which are operated by Variable Frequency Drive (VFD). This whole dataset is a time series and is handled accordingly. The cleaned dataset is analyzed to find different patterns and investigate relations between different data points. The analysis helps us in choosing parameters for models that are developed in the next step. Different statistical models are developed to simulate building and equipment behavior. Finally, the models along with the data are used to optimize the building Operation with the equipment constraints to make decisions for building operation which leads to a reduction in energy consumption while maintaining temperature and pressure inside the building.
Book Synopsis Intelligent Data-Driven Modelling and Optimization in Power and Energy Applications by : B Rajanarayan Prusty
Download or read book Intelligent Data-Driven Modelling and Optimization in Power and Energy Applications written by B Rajanarayan Prusty and published by CRC Press. This book was released on 2024-05-09 with total page 253 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a comprehensive understanding of how intelligent data-driven techniques can be used for modelling, controlling, and optimizing various power and energy applications. It aims to develop multiple data-driven models for forecasting renewable energy sources and to interpret the benefits of these techniques in line with first-principles modelling approaches. By doing so, the book aims to stimulate deep insights into computational intelligence approaches in data-driven models and to promote their potential applications in the power and energy sectors. Its key features include: an exclusive section on essential preprocessing approaches for the data-driven model a detailed overview of data-driven model applications to power system planning and operational activities specific focus on developing forecasting models for renewable generations such as solar PV and wind power, and showcasing the judicious amalgamation of allied mathematical treatments such as optimization and fractional calculus in data-driven model-based frameworks This book presents novel concepts for applying data-driven models, mainly in the power and energy sectors, and is intended for graduate students, industry professionals, research, and academic personnel.
Book Synopsis Engaging Beyond the Meter by : Bronwyn Mary Lazowski
Download or read book Engaging Beyond the Meter written by Bronwyn Mary Lazowski and published by . This book was released on 2019 with total page 325 pages. Available in PDF, EPUB and Kindle. Book excerpt: With scientists around the world indicating a brief window of opportunity for reducing irreversible climate change impacts, the time has never been more pressing for sustainability transitions (IPCC, 2018). The role of energy is especially important in these developments, where anthropogenic forces have created a "... twin energy and climate nexus," (Van De Graaf, 2013, p. 42) as a result of the extraction, production, and consumption of energy resources. At a global scale, 78% of human-induced greenhouse gas (GHG) emissions are from energy production and consumption (Natural Resources Canada, 2018a). Therefore, clean energy developments are an essential element of international climate goals. A key element of clean energy developments is energy conservation and demand management. With Canada having one of the world's largest per capita electricity consumption rates, increased end-use management is essential to reduce system-level pressures within clean energy developments (International Energy Agency, 2018). Significant opportunities for electricity management exist in the residential sector, which contributes to 27% of international electricity consumption (International Energy Agency, 2017). This is especially the case in Canada, where the residential sector contributes to 34% of national electricity use, emitting 21.4 Mt of CO2e (Natural Resources Canada, 2019b, 2019a). Therefore, there is a strong need to transform Canada's residential consumption management and practices to benefit national climate change objectives. Technological innovations in the modern energy grid deliver new opportunities for clean energy developments. Specifically, the smart grid creates two-way flows of both data and energy, thereby transforming technological capabilities and end-user roles. Beginning in 2004, the Province of Ontario facilitated large-scale smart metering implementation to enable a 'conservation culture,' consequently, becoming a prominent testing ground for residential smart grid development. Although the smart grid offers new technological potential, investigating 'beyond' the meter and into end-user engagement is critical for making these large-scale shifts. Social science research applications have previously remained underrepresented in energy literature and deliver novel opportunities for studying smart grid engagement. The holistic and scalable energy cultures framework presents a comprehensive approach to study the complexity of residential energy management, with substantial opportunities for applications in smart grid research (Stephenson et al., 2010). This dissertation, entitled 'Engaging beyond the meter: Encouraging residential energy management using smart grid tools,' delivers novel contributions to residential smart grid and engagement research for developing insights on household engagement and energy management. Drawing from the literatures on smart grid interventions, social science energy research, and consumer engagement, this dissertation utilizes two Ontario residential smart grid case studies to assess the potential of smart grid technologies to facilitate consumption changes. Additionally, this dissertation incorporates a comprehensive review of existing approaches for intervention design and proposes a novel integrated engagement model for shifting consumer cultures towards sustainability. This dissertation research is presented in four distinct yet interrelated manuscripts. Chapter 4 investigates the impacts of smart grid interventions on household energy cultures during a multi-year residential smart grid case study, following participant interviews. The energy cultures framework is applied to identify the nuances surrounding household energy management, specifically the changes in norms, practices, and materials. Additionally, qualitative feedback on the effectiveness of these smart grid engagement mechanisms for household energy management is collected. The results identify the challenges surrounding household energy management in relation to smart grid developments and present a novel application of the energy cultures framework within the Canadian residential smart grid. Chapter 5 further examines the impact of two smart grid interventions (electricity report and mobile tablet) to re-engage consumers over the multi-year residential smart grid project. This study examines whole-house and appliance-level consumption data alongside participant interviews. As a result, this study determines whether re-engagement influenced consumption, highlights contributing energy management practices (e.g., cooking, laundry, entertaining, air conditioning, dishwashing), and determines underlying factors influencing energy management. Significant conservation and peak shifting in laundry consumption were identified during a 10-week autumn period. User experience interviews highlighted the preference for weekly reports over a tablet for re-engagement. Therefore, this chapter provides unique perspectives for long-term engagement and re-engagement in the smart grid for the promotion of lasting residential energy management. Chapter 6 assesses the influence of a large-scale introduction of in-home displays (IHDs) to central Ontario homes. Two years of hourly consumption data for IHD recipients (n=5274) are analyzed and compared to a control group (n=3020) to determine changes in conjunction with IHD feedback at population and cohort levels. Consumer segments incorporating behavioural (load-shape) and thermal consumption patterns were identified. Following an impact assessment, no significant impacts were experienced in the general population; however, specific consumer segments responded favourably by conservation or peak shifting. These notable segments only represented 12% of the IHD recipients and had evening peak and heating thermal consumption profiles. This study emphasizes the importance of effective program design that utilizes comprehensive datasets, user-centred approaches, consumer targeting, and multiple mechanisms extending 'beyond feedback.' This chapter also highlights opportunities for utilizing 'big' smart metering data to understand consumers and their energy practices using quantitative methods. Chapter 7 presents a novel model for intervention design for sustainability as an outcome of a conceptual review. The proposed ENGAAGGE model presents an integrated model for intervention design that bridges the limitations from the current disciplinary silos for collective change. The paper provides a comprehensive review of existing intervention approaches (social marketing, community based social marketing, social practice theory, and design thinking), highlights the key elements for intervention design, and proposes the ENGAAGGE model that incorporates the strengths of existing approaches, while addressing their respective limitations. Therefore, the outcomes of this chapter provide innovative opportunities for application in future research and practice for collective change. This dissertation research brings novel contributions to theory and practice. First, this research provides an innovative application of the energy cultures framework to the residential smart grid and delivers a new framing for a smart and sustainable energy culture. The holistic understanding developed from applying this framework delivers insights for household smart grid engagement applicable to future program design. Second, the IHD segmentation analysis extends research on smart grid-enabled feedback and consumer response by the combination of a large-scale cohort and consumer segmentation. The research outcomes deliver critical recommendations for future programming to include consumer targeting and user-centred design. Third, the longevity and mixed-methods approach of the EHMS study provides novel and detailed contributions to smart grid energy cultures and engagement research to test with broader audiences. These outcomes provide insights for consumer engagement for long-term engagement and re-engagement relevant for residential smart grid programming. Fourth, the conceptual review and integrated model presented in Chapter 7 bring critical contributions to the sustainability engagement literature and provide substantial opportunities for application in future research and practice. In conclusion, this dissertation research delivers novel contributions to smart grid research for engaging consumers beyond the meter.
Book Synopsis Investigating the Human Behavior Side of Building Energy Efficiency by : Chao Chen
Download or read book Investigating the Human Behavior Side of Building Energy Efficiency written by Chao Chen and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: We describe and evaluate each of these contributions using electricity consumption data from actual smart homes as part of the CASAS smart home project. In each case we illustrate the efficacy of these algorithms to gaining insights on human behavior and its impact on energy consumption, and offer ideas for using these insights to promote sustainable behaviors.
Book Synopsis Big Data and Security by : Yuan Tian
Download or read book Big Data and Security written by Yuan Tian and published by Springer Nature. This book was released on 2022-03-09 with total page 761 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book constitutes the refereed proceedings of the Third International Conference on Big Data and Security, ICBDS 2021, held in Shenzhen, China, in November 2021 The 46 revised full papers and 13 short papers were carefully reviewed and selected out of 221 submissions. The papers included in this volume are organized according to the topical sections on cybersecurity and privacy; big data; blockchain and internet of things, and artificial intelligence/ machine learning security.
Book Synopsis Energy Efficiency Analysis and Intelligent Optimization of Process Industry by : Zhiqiang Geng
Download or read book Energy Efficiency Analysis and Intelligent Optimization of Process Industry written by Zhiqiang Geng and published by Frontiers Media SA. This book was released on 2023-10-09 with total page 153 pages. Available in PDF, EPUB and Kindle. Book excerpt:
