Download or read book Disaggregating Time Series Data for Energy Consumption by Aggregate and Individual Customer written by Steven Vitullo and published by . This book was released on 2010 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation generalizes the problem of disaggregating time series data and describes the disaggregation problem as a mathematical inverse problem that breaks up aggregated (measured) time series data that is accumulated over an interval and estimates its component parts. We describe five different algorithms for disaggregating time series data: the Naive, Time Series Reconstruction (TSR), Piecewise Linear Optimization (PLO), Time Series Reconstruction with Resampling (RS), and Interpolation (INT). The TSR uses least squares and domain knowledge of underlying correlated variables to generate underlying estimates and handles arbitrarily aggregated time steps and non-uniformly aggregated time steps. The PLO performs an adjustment on underlying estimates so the sum of the underlying estimated data values within an interval are equal to the aggregated data value. The RS repeatedly samples a subset of our data, and the fifth algorithm uses an interpolation to estimate underlying estimated data values. Several methods of combining these algorithms, taken from the forecasting domain, are applied to improve the accuracy of the disaggregated time series data. We evaluate our component and ensemble algorithms in three different applications: disaggregating aggregated (monthly) gas consumption into disaggregated (daily) gas consumption from natural gas regional areas (operating areas), disaggregating United States Gross Domestic Product (GDP) from yearly GDP to quarterly GDP, and forecasting when a truck should fill a customer's heating oil tank. We show our five algorithms successfully used to disaggregate historical natural gas consumption and GDP, and we show combinations of these algorithms can improve further the magnitude and variability of the natural gas consumption or GDP series. We demonstrate that the PLO algorithm is the best of the Naive, TSR, and PLO algorithms when disaggregating GDP series. Finally, ex-post results using the Naive, TSR, PLO, RS, INT, and the ensemble algorithms when applied to forecast heating oil deliveries are shown. Results show the Equal Weight (EW) combination of the Naive, TSR, PLO, RS, and INT algorithms outperforms the forecasting system Company YOU used before approaching the gasdayTM laboratory at Marquette University, and comes close, but does not outperform existing techniques the GasDayTM laboratory has implemented to forecast heating oil deliveries.

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.

Download or read book Systems Engineering for Power written by United States. Division of Electric Energy Systems. Systems Management & Structuring and published by . This book was released on 1980 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book DOE RA written by and published by . This book was released on 1980 with total page 204 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Disaggregating Times Series Data written by and published by . This book was released on 1997 with total page 27 pages. Available in PDF, EPUB and Kindle. Book excerpt: This report describes our experiences with disaggregating time series data. Suppose we have gathered data every two seconds and want to guess the data at one-second intervals. Under certain assumptions, there are several reasonable disaggregation methods as well as several performance measures to judge their performance. Here we present results for both simulated and real data for two methods using several performance criteria.

Download or read book Analysis of Energy Disaggregation Techniques in Non intrusive Appliance Load Monitoring written by Jihad Sadallah Ashkar and published by . This book was released on 2016 with total page 96 pages. Available in PDF, EPUB and Kindle. Book excerpt: Carbon dioxide emission reduction goals have intensified interest in researching new methods to improve our efficient use of electricity. It has been proven that providing consumers with appliance usage patterns can have significant energy savings. Non-intrusive appliance load monitoring (NIALM) research aims to facilitate the large scale installation of mechanisms that provide such usage information. NIALM is the process of using the whole home electricity signal to determine the energy consumption information of appliances in the home without direct measurement. In this paper, we propose a fast and efficient non-parametric technique for disaggregating the whole home energy signal to determine individual appliance power consumption with high precision. We evaluate our proposed technique with the REDD dataset and show that it performs better than existing approaches in practice. We also propose modifications to known sparse coding techniques for energy disaggregation. Lastly, we evaluate the feasibility of employing Gaussian Process Regression for the purpose of NIALM.

Download or read book Energy Informatics written by Bo Nørregaard Jørgensen and published by Springer Nature. This book was released on 2023-12-01 with total page 313 pages. Available in PDF, EPUB and Kindle. Book excerpt: This two-volume set LNCS 14467-14468 constitutes the proceedings of the First Energy Informatics Academy Conference, EI.A 2023,held in Campinas, Brazil, in December 2023. The 39 full papers together with 8 short papers included in these volumes were carefully reviewed and selected from 53 submissions. The conference focuses on the application of digital technology and information management to facilitate the global transition towards sustainable and resilient energy systems.

Download or read book The California Electricity Crisis written by Christopher Weare and published by Public Policy Instit. of CA. This book was released on 2003 with total page 140 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Routledge Handbook of Energy Economics written by Uğur Soytaş and published by Routledge. This book was released on 2019-09-23 with total page 620 pages. Available in PDF, EPUB and Kindle. Book excerpt: Energy consumption and production have major influences on the economy, environment, and society, but in return they are also influenced by how the economy is structured, how the social institutions work, and how the society deals with environmental degradation. The need for integrated assessment of the relationship between energy, economy, environment, and society is clear, and this handbook offers an in-depth review of all four pillars of the energy-economy-environment-society nexus. Bringing together contributions from all over the world, this handbook includes sections devoted to each of the four pillars. Moreover, as the financialization of commodity markets has made risk analysis more complicated and intriguing, the sections also cover energy commodity markets and their links to other financial and non-financial markets. In addition, econometric modeling and the forecasting of energy needs, as well as energy prices and volatilities, are also explored. Each part emphasizes the multidisciplinary nature of the energy economics field and from this perspective, chapters offer a review of models and methods used in the literature. The Routledge Handbook of Energy Economics will be of great interest to all those studying and researching in the area of energy economics. It offers guideline suggestions for policy makers as well as for future research.

Download or read book Non Intrusive Load Monitoring Using Additive Time Series Modeling Via Finite Mixture Models Aggregation written by Soudabeh Tabarsaii and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to an exponential rise in energy consumption, it is imperative that buildings adopt sustainable energy consumption systems. A number of studies have shown that this can be achieved by providing real-time feedback on the energy consumption of each appliance to residents. It is possible to accomplish this through non-intrusive load monitoring (NILM) that disaggregates electricity consumption of individual appliances from the total energy consumption of a household. Research on NILM typically trains the inference model for a single house which cannot be generalized to other houses. In this Master thesis, a novel approach is proposed to tackle mentioned issue.This thesis proposes to use two finite mixture models namely generalized Gaussian mixture and Gamma mixture, to create a generalizable electrical signature model for each appliance type by training over labelled data and create various combinations of appliances together. By using this strategy, a model can be used on unseen houses, without extensive training on the new house. The issue of different measurement resolutions in the NILM area is also a considerable challenge. As a rule of thumb, state-of-the-art methods are studied using high-frequency data, which is rarely applicable in real-world situations due to smart meters' limited precision. To address this issue, the model is evaluated on three different datasets with different timestamps, AMPds, REDD and IRISE datasets. To increase the aggregation level and compare with RNN and FHMM as two well-known methods in NILM, an extension that we called DNN-Mixtures, is proposed. The results show that the proposed model can compete with state of art techniques. For evaluation, accuracy, precision, recall and F-score metrics are used.

Download or read book A Statistical Approach for Disaggregating Mixed frequency Economic Time Series Data written by Wai-Sum Chan and published by . This book was released on 1997 with total page 14 pages. Available in PDF, EPUB and Kindle. Book excerpt: "The problem of mixed-frequency time series data arises from changing the observation frequency. For example, we may have a time series with quarterly observations in the first portion and annual figures in the remainder. We shall call that quarter-year mixed-frequency data. In this paper we suggest a method to disaggregate the annual observations to quarterly values. The proposed method can easily be generalised to the year-quarter, quarter-month, year-month and other mixed-frequency situations; it may avoid difficulties of time series modelling and is easy to implement. A step-by-step algorithm of the method is given so that econometricians not expert in this area can still perform the procedure. The proposed method is illustrated through two real examples. We also conduct a small scale Monte Carlo experiment to compare the proposed procedure with two existing alternative methods. Finally, some concluding remarks are given"--Abstract.

Download or read book Energy Use in Cities written by Stephanie Pincetl and published by Springer Nature. This book was released on 2020-09-25 with total page 191 pages. Available in PDF, EPUB and Kindle. Book excerpt: In an era of big data and smart cities, this book is an innovative and creative contribution to our understanding of urban energy use. Societies have basic data needs to develop an understanding of energy flows for planning energy sustainability. However, this data is often either not utilized or not available. Using California as an example, the book provides a roadmap for using data to reduce urban greenhouse gas emissions by targeting programs and initiatives that will successfully and parsimoniously improve building performance while taking into account issues of energy affordability. This first of its kind methodology maps high-detail building energy use to understand patterns of consumption across buildings, neighborhoods, and socioeconomic divisions in megacities. The book then details the steps required to replicate this methodology elsewhere, and shows the importance of openly-accessible building energy data for transitioning cities to meet the climate planning goals of the twenty-first century. It also explains why actual data, not modeled or sampled, is critical for accurate analysis and insights. Finally, it acknowledges the complex institutional context for this work and some of the obstacles – utility reluctance, public agency oversight, funding and path dependencies. This book will be of great value to scholars across the environmental sectors, but especially to those studying sustainable urban energy as well as practitioners and policy makers in these areas.

Download or read book Practical Guidebook on Data Disaggregation for the Sustainable Development Goals written by Asian Development Bank and published by Asian Development Bank. This book was released on 2021-05-01 with total page 137 pages. Available in PDF, EPUB and Kindle. Book excerpt: The "leave no one behind" principle espoused by the 2030 Agenda for Sustainable Development requires measures of progress for different segments of the population. This entails detailed disaggregated data to identify subgroups that might be falling behind, to ensure progress toward achieving the Sustainable Development Goals (SDGs). The Asian Development Bank and the Statistics Division of the United Nations Department of Economic and Social Affairs developed this practical guidebook with tools to collect, compile, analyze, and disseminate disaggregated data. It also provides materials on issues and experiences of countries regarding data disaggregation for the SDGs. This guidebook is for statisticians and analysts from planning and sector ministries involved in the production, analysis, and communication of disaggregated data.

Download or read book Disaggregate Consumption Feedback and Energy Conservation written by Andreas Gerster and published by . This book was released on 2020 with total page 40 pages. Available in PDF, EPUB and Kindle. Book excerpt: Novel information technologies hold the promise to improve decision making. In the context of smart metering, we investigate the impact of providing households with appliance-level electricity feedback. In a randomized controlled trial, we find that the provision of appliance-level feedback creates a conservation effect of an additional 5% relative to a group receiving standard (aggregate) feedback. These conservation effects are largely driven by reductions in electricity use of 10% to 15% during peak hours. Consumers with appliance-level feedback hold more accurate beliefs about the energy consumption of different appliances, consistent with the mechanism in our accompanying model. Our result suggests that conservation effects from a smart-meter rollout will be much larger if appliance-level feedback can be provided. Based on a sufficient statistics approach, we estimate that appliance-level feedback could raise consumer surplus by about 570 to 600 million Euro per annum for German households.

Download or read book Practical Applications of Intelligent Systems written by Zhenkun Wen and published by Springer. This book was released on 2014-07-18 with total page 1132 pages. Available in PDF, EPUB and Kindle. Book excerpt: "Practical Applications of Intelligent Systems" presents selected papers from the 2013 International Conference on Intelligent Systems and Knowledge Engineering (ISKE2013). The aim of this conference is to bring together experts from different expertise areas to discuss the state-of-the-art in Intelligent Systems and Knowledge Engineering, and to present new research results and perspectives on future development. The topics in this volume include, but are not limited to: Intelligent Game, Intelligent Multimedia, Business Intelligence, Intelligent Bioinformatics Systems, Intelligent Healthcare Systems, User Interfaces and Human Computer Interaction, Knowledge-based Software Engineering, Social Issues of Knowledge Engineering, etc. The proceedings are benefit for both researchers and practitioners who want to learn more about the current practice, experience and promising new ideas in the broad area of intelligent systems and knowledge engineering. Dr. Zhenkun Wen is a Professor at the College of Computer and Software Engineering, Shenzhen University, China. Dr. Tianrui Li is a Professor at the School of Information Science and Technology, Southwest Jiaotong University, Xi’an, China.

Download or read book Big Data Application in Power Systems written by Reza Arghandeh and published by Elsevier. This book was released on 2024-07-01 with total page 450 pages. Available in PDF, EPUB and Kindle. Book excerpt: Big Data Application in Power Systems, Second Edition presents a thorough update of the previous volume, providing readers with step-by-step guidance in big data analytics utilization for power system diagnostics, operation, and control. Bringing back a team of global experts and drawing on fresh, emerging perspectives, this book provides cutting-edge advice for meeting today’s challenges in this rapidly accelerating area of power engineering. Divided into three parts, this book begins by breaking down the big picture for electric utilities, before zooming in to examine theoretical problems and solutions in detail. Finally, the third section provides case studies and applications, demonstrating solution troubleshooting and design from a variety of perspectives and for a range of technologies. Readers will develop new strategies and techniques for leveraging data towards real-world outcomes. Including five brand new chapters on emerging technological solutions, Big Data Application in Power Systems, Second Edition remains an essential resource for the reader aiming to utilize the potential of big data in the power systems of the future. Provides a total refresh to include the most up-to-date research, developments, and challenges Focuses on practical techniques, including rapidly modernizing monitoring systems, measurement data availability, big data handling and machine learning approaches for processing high dimensional, heterogeneous, and spatiotemporal data Engages with cross-disciplinary lessons, drawing on the impact of intersectional technology including statistics, computer science, and bioinformatics Includes five brand new chapters on hot topics, ranging from uncertainty decision-making to features, selection methods, and the opportunities provided by social network data

Download or read book Asymptotic Behavior of Time Series Aggregates written by G. C. Tiao and published by . This book was released on 1972 with total page 22 pages. Available in PDF, EPUB and Kindle. Book excerpt: The paper discusses the efficiency of disaggregation in forecasting time series aggregates. Let be the disaggregated series and XT+(ZMT-m+1 + ... + ZMT) be the m-component aggregated series. Forecasts of future XT may be constructed from data on (i) Zt or (ii) XT. It is shown that, for large m, there is no gain in using the disaggreagated data if Zt is stationary, but dramatic gain can be obtained when Zt is non-stationary. (Author).