Download or read book Modelling and Forecasting Electricity Demand Using Aggregate and Disaggregate Data written by Gordon Ivan Dodds and published by . This book was released on 1988 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Modelling and Forecasting Electricity Demand Using Aggregate and Disaggregrate Data written by Ivan Gordon Dodds and published by . This book was released on 1988 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Modeling and Forecasting Electricity Demand written by Kevin Berk and published by Springer. This book was released on 2015-01-20 with total page 123 pages. Available in PDF, EPUB and Kindle. Book excerpt: The master thesis of Kevin Berk develops a stochastic model for the electricity demand of small and medium-sized companies that is flexible enough so that it can be used for various business sectors. The model incorporates the grid load as an exogenous factor and seasonalities on a daily, weekly and yearly basis. It is demonstrated how the model can be used e.g. for estimating the risk of retail contracts. The uncertainty of electricity demand is an important risk factor for customers as well as for utilities and retailers. As a consequence, forecasting electricity load and its risk is now an integral component of the risk management for all market participants.
Download or read book Demand Forecasting for Electric Utilities written by Clark W. Gellings and published by . This book was released on 1992 with total page 552 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Modeling and Forecasting Electricity Loads and Prices written by Rafal Weron and published by John Wiley & Sons. This book was released on 2007-01-30 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book offers an in-depth and up-to-date review of different statistical tools that can be used to analyze and forecast the dynamics of two crucial for every energy company processes—electricity prices and loads. It provides coverage of seasonal decomposition, mean reversion, heavy-tailed distributions, exponential smoothing, spike preprocessing, autoregressive time series including models with exogenous variables and heteroskedastic (GARCH) components, regime-switching models, interval forecasts, jump-diffusion models, derivatives pricing and the market price of risk. Modeling and Forecasting Electricity Loads and Prices is packaged with a CD containing both the data and detailed examples of implementation of different techniques in Matlab, with additional examples in SAS. A reader can retrace all the intermediate steps of a practical implementation of a model and test his understanding of the method and correctness of the computer code using the same input data. The book will be of particular interest to the quants employed by the utilities, independent power generators and marketers, energy trading desks of the hedge funds and financial institutions, and the executives attending courses designed to help them to brush up on their technical skills. The text will be also of use to graduate students in electrical engineering, econometrics and finance wanting to get a grip on advanced statistical tools applied in this hot area. In fact, there are sixteen Case Studies in the book making it a self-contained tutorial to electricity load and price modeling and forecasting.
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.
Download or read book U S Regional Demand Forecasts Using NEMS and GIS written by Chris Marnay and published by . This book was released on 2005 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: The National Energy Modeling System (NEMS) is a multi-sector, integrated model of the U.S. energy system put out by the Department of Energy's Energy Information Administration. NEMS is used to produce the annual 20-year forecast of U.S. energy use aggregated to the nine-region census division level. The research objective was to disaggregate this regional energy forecast to the county level for select forecast years, for use in a more detailed and accurate regional analysis of energy usage across the U.S. The process of disaggregation using a geographic information system (GIS) was researched and a model was created utilizing available population forecasts and climate zone data. The model's primary purpose was to generate an energy demand forecast with greater spatial resolution than what is currently produced by NEMS, and to produce a flexible model that can be used repeatedly as an add-on to NEMS in which detailed analysis can be executed exogenously with results fed back into the NEMS data flow. The methods developed were then applied to the study data to obtain residential and commercial electricity demand forecasts. The model was subjected to comparative and statistical testing to assess predictive accuracy. Forecasts using this model were robust and accurate in slow-growing, temperate regions such as the Midwest and Mountain regions. Interestingly, however, the model performed with less accuracy in the Pacific and Northwest regions of the country where population growth was more active. In the future more refined methods will be necessary to improve the accuracy of these forecasts. The disaggregation method was written into a flexible tool within the ArcGIS environment which enables the user to output the results in five year intervals over the period 2000-2025. In addition, the outputs of this tool were used to develop a time-series simulation showing the temporal changes in electricity forecasts in terms of absolute, per capita, and density of demand.
Download or read book Statistical Learning Tools for Electricity Load Forecasting written by Anestis Antoniadis and published by Birkhäuser. This book was released on 2024-09-21 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This monograph explores a set of statistical and machine learning tools that can be effectively utilized for applied data analysis in the context of electricity load forecasting. Drawing on their substantial research and experience with forecasting electricity demand in industrial settings, the authors guide readers through several modern forecasting methods and tools from both industrial and applied perspectives – generalized additive models (GAMs), probabilistic GAMs, functional time series and wavelets, random forests, aggregation of experts, and mixed effects models. A collection of case studies based on sizable high-resolution datasets, together with relevant R packages, then illustrate the implementation of these techniques. Five real datasets at three different levels of aggregation (nation-wide, region-wide, or individual) from four different countries (UK, France, Ireland, and the USA) are utilized to study five problems: short-term point-wise forecasting, selection of relevant variables for prediction, construction of prediction bands, peak demand prediction, and use of individual consumer data. This text is intended for practitioners, researchers, and post-graduate students working on electricity load forecasting; it may also be of interest to applied academics or scientists wanting to learn about cutting-edge forecasting tools for application in other areas. Readers are assumed to be familiar with standard statistical concepts such as random variables, probability density functions, and expected values, and to possess some minimal modeling experience.
Download or read book Forecasting Electricity Demand in the Industrial Sector Based on Disaggregate Data written by Peter McCafferty and published by . This book was released on 1991 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Mathematical Modelling of Contemporary Electricity Markets written by Athanasios Dagoumas and published by Academic Press. This book was released on 2021-01-30 with total page 444 pages. Available in PDF, EPUB and Kindle. Book excerpt: Mathematical Modelling of Contemporary Electricity Markets reviews major methodologies and tools to accurately analyze and forecast contemporary electricity markets in a ways that is ideal for practitioner and academic audiences. Approaches include optimization, neural networks, genetic algorithms, co-optimization, econometrics, E3 models and energy system models. The work examines how new challenges affect power market modeling, including discussions of stochastic renewables, price volatility, dynamic participation of demand, integration of storage and electric vehicles, interdependence with other commodity markets and the evolution of policy developments (market coupling processes, security of supply). Coverage addresses all major forms of electricity markets: day-ahead, forward, intraday, balancing, and capacity. Provides a diverse body of established techniques suitable for modeling any major aspect of electricity markets Familiarizes energy experts with the quantitative skills needed in competitive electricity markets Reviews market risk for energy investment decisions by stressing the multi-dimensionality of electricity markets
Download or read book Reference Manual of Data Sources for Load Forecasting written by and published by . This book was released on 1981 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Development of a Data Base and Forecasting Model for Commercial Sector Electricity Usage and Demand written by Hittman Associates and published by . This book was released on 1980 with total page 62 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Development of a Commercial Sector Data Base and Forecasting Model for Electricity Usage and Demand written by Hittman Associates and published by . This book was released on 1980 with total page 42 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Long range Forecasting Properties of State of the art Models of Demand for Electric Energy written by William H. Hieronymus and published by . This book was released on 1976 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This report evaluates the long-range forecasting effectiveness of selected econometric models of the demand for electric energy. The eight models tested represent those specifications and other features of extant models believed to be of potential use in developing improved models for long-range forecasting at the national and regional levels. These include the residential models of Anderson (4); Halvorsen (40); Houthakker et al.(46) and Mount et al. (66); the commercial model of Mount et al.(66); the industrial models of Fisher and Kaysen (33); Mount et al.(66); and an extension of Anderson's industrial model (1).Each model is replicated, reestimated on a common data set and tested for performance: forecast and back cast accuracy, parameter stability over time, robustness of parameter estimates to small changes in specification or variable measurement, consistency and plausibility of model results, and quality of model test statistics. Some approaches are found to be clearly superior to others as a basis for long-range forecasting, but the effectiveness of all of the models is limited by the quality of the available data and their reliance for estimation on pooled cross section/timeseries of statewide aggregate measures during a period of relatively smooth growth. The resulting problems of multicollinearity and lack of observed variance in key variables contribute to uncertain and unstable estimates. The final chapter of the report presents recommendations for near -term and longer -range improvement in the state of the art. Many of these improvements await availability of better data: for example, greater disaggregation of electricity data by end use and end user, improved fuel price and availability data, better data on stocks of appliances and buildings. An annotated bibliography of long-term electric energy forecasting models is presented in a separate volume.
Download or read book Data Analytics in Power Markets written by Qixin Chen and published by Springer Nature. This book was released on 2021-10-01 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book aims to solve some key problems in the decision and optimization procedure for power market organizers and participants in data-driven approaches. It begins with an overview of the power market data and analyzes on their characteristics and importance for market clearing. Then, the first part of the book discusses the essential problem of bus load forecasting from the perspective of market organizers. The related works include load uncertainty modeling, bus load bad data correction, and monthly load forecasting. The following part of the book answers how much information can be obtained from public data in locational marginal price (LMP)-based markets. It introduces topics such as congestion identification, componential price forecasting, quantifying the impact of forecasting error, and financial transmission right investment. The final part of the book answers how to model the complex market bidding behaviors. Specific works include pattern extraction, aggregated supply curve forecasting, market simulation, and reward function identification in bidding. These methods are especially useful for market organizers to understand the bidding behaviors of market participants and make essential policies. It will benefit and inspire researchers, graduate students, and engineers in the related fields.
Download or read book Reference Manual of Data Sources for Load Forecasting written by L. Andrews and published by . This book was released on 1981 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This report examines the quality and availability of data for use in electricity load forecast models. It documents the data sources available for more than 100 variables used in these forecast models, and evaluates the quality of the data. Also, a description of aggregate econometric and disaggregate econometric or end-use models is presented.
Download or read book Forecasting U S Electricity Demand written by Adela Maria Bolet and published by Routledge. This book was released on 2019-08-30 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: Although the energy headlines of 1985 proclaim the waning of OPEC, the collapse of oil prices, and the demise of the nuclear power industry, few policy analysts are examining the dynamic challenges and opportunities that may confront the electric power industry during the remainder of this century. In this pioneering work, Adela Maria Bolet attempts to do exactly this, namely, to reconcile the differences among forecasters as to the future of electricity demand in the industrial, commercial, and residential sectors.
