Download or read book Modeling and Forecasting Electricity Demand written by Kevin Berk and published by Springer Spektrum. This book was released on 2015-01-30 with total page 0 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 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 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 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.
Download or read book Electric Load Forecasting written by Stanford University. Energy Modeling Forum and published by . This book was released on 1980 with total page 430 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Modeling and Analysis of Electricity Demand by Time of day written by Rocco Fazzolare and published by . This book was released on 1978 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This report includes several papers on modeling and forecasting electricity demands by time -of -day that were presented at a workshop in San Diego, June 11-14, 1978.The papers and the accompanying discussants' comments present a cross section of the state of the art in research on the responsiveness of electricity demands to time -of -day rates. Preliminary analyses of several residential peak -load -pricing experiments present diverse estimates of the responsiveness of household electricity demand to time -of -day prices. As yet, there are few results that are directly applicable to utility forecasting and planning, however these analyses undoubtedly lay the foundation for useful results in the near future. There is only a small amount of data and even less analysis on the price responsiveness of load patterns in the commercial and industrial sectors. The volume is concluded with several insightful commentators' overviews of where the state of the art is and where it ought to be extended.
Download or read book Modeling and Forecasting Electricity Consumption Amid the COVID 19 Pandemic written by Lanouar Charfeddine and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Accurately modelling and forecasting electricity consumption is a key prerequisite for strategic sustainable energy planning and development. In this study, we use four advanced econometrics time series models and four machine learning (ML) and deep learning models including an AR with seasonality, ARX, ARFIMAX, 3S-MSARX, Prophet, XGBoost, LSTM and SVR to analyze and forecast electricity consumption during COVID-19 pre-lockdown, lockdown, releasing-lockdown, and post-lockdown phases. We use monthly data on Qatar's total electricity consumption from January 2010 to December 2021. The empirical findings demonstrate that both econometric and ML models can capture most of the important statistical features characterizing electricity consumption (e.g., seasonality, sudden changes, outliers, trend, and potential long-lasting impact of shocks). In particular, we find that climate change based factors, e.g temperature, rainfall, mean sea-level pressure and wind speed, are key determinants of electricity consumption. In terms of forecasting, the results indicate that the ARFIMAX(1,d,0) and the 3S-MSARX(1) models outperform all other models. Policy implications and energy-environmental recommendations are proposed and discussed.
Download or read book Forecasting and Assessing Risk of Individual Electricity Peaks written by Maria Jacob and published by Springer Nature. This book was released on 2019-09-25 with total page 108 pages. Available in PDF, EPUB and Kindle. Book excerpt: The overarching aim of this open access book is to present self-contained theory and algorithms for investigation and prediction of electric demand peaks. A cross-section of popular demand forecasting algorithms from statistics, machine learning and mathematics is presented, followed by extreme value theory techniques with examples. In order to achieve carbon targets, good forecasts of peaks are essential. For instance, shifting demand or charging battery depends on correct demand predictions in time. Majority of forecasting algorithms historically were focused on average load prediction. In order to model the peaks, methods from extreme value theory are applied. This allows us to study extremes without making any assumption on the central parts of demand distribution and to predict beyond the range of available data. While applied on individual loads, the techniques described in this book can be extended naturally to substations, or to commercial settings. Extreme value theory techniques presented can be also used across other disciplines, for example for predicting heavy rainfalls, wind speed, solar radiation and extreme weather events. The book is intended for students, academics, engineers and professionals that are interested in short term load prediction, energy data analytics, battery control, demand side response and data science in general.
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 Regional Econometric Model for Forecasting Electricity Demand by Sector and by State written by Oak Ridge National Laboratory. Energy Division and published by . This book was released on 1978 with total page 200 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Forecasting Models of Electricity Prices written by Javier Contreras and published by MDPI. This book was released on 2018-04-06 with total page 259 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is a printed edition of the Special Issue "Forecasting Models of Electricity Prices" that was published in Energies
Download or read book Modeling and Analysis of Electricity Demand by Time of day written by Rocco Fazzolare and published by . This book was released on 1979 with total page 464 pages. Available in PDF, EPUB and Kindle. Book excerpt:
Download or read book Electrical Load Forecasting written by S.A. Soliman and published by Elsevier. This book was released on 2010-05-26 with total page 441 pages. Available in PDF, EPUB and Kindle. Book excerpt: Succinct and understandable, this book is a step-by-step guide to the mathematics and construction of electrical load forecasting models. Written by one of the world’s foremost experts on the subject, Electrical Load Forecasting provides a brief discussion of algorithms, their advantages and disadvantages and when they are best utilized. The book begins with a good description of the basic theory and models needed to truly understand how the models are prepared so that they are not just blindly plugging and chugging numbers. This is followed by a clear and rigorous exposition of the statistical techniques and algorithms such as regression, neural networks, fuzzy logic, and expert systems. The book is also supported by an online computer program that allows readers to construct, validate, and run short and long term models. Step-by-step guide to model construction Construct, verify, and run short and long term models Accurately evaluate load shape and pricing Creat regional specific electrical load models
Download or read book Short Term Load Forecasting 2019 written by Antonio Gabaldón and published by MDPI. This book was released on 2021-02-26 with total page 324 pages. Available in PDF, EPUB and Kindle. Book excerpt: Short-term load forecasting (STLF) plays a key role in the formulation of economic, reliable, and secure operating strategies (planning, scheduling, maintenance, and control processes, among others) for a power system and will be significant in the future. However, there is still much to do in these research areas. The deployment of enabling technologies (e.g., smart meters) has made high-granularity data available for many customer segments and to approach many issues, for instance, to make forecasting tasks feasible at several demand aggregation levels. The first challenge is the improvement of STLF models and their performance at new aggregation levels. Moreover, the mix of renewables in the power system, and the necessity to include more flexibility through demand response initiatives have introduced greater uncertainties, which means new challenges for STLF in a more dynamic power system in the 2030–50 horizon. Many techniques have been proposed and applied for STLF, including traditional statistical models and AI techniques. Besides, distribution planning needs, as well as grid modernization, have initiated the development of hierarchical load forecasting. Analogously, the need to face new sources of uncertainty in the power system is giving more importance to probabilistic load forecasting. This Special Issue deals with both fundamental research and practical application research on STLF methodologies to face the challenges of a more distributed and customer-centered power system.
Download or read book Forecasting New Jersey s Electricity Demand Using Auto Regressive Models written by Shankar Chandramowli and published by . This book was released on 2013 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Forecasting is an important tool in planning and policy making. Electricity load forecasting is necessary for power systems planning, efficient dispatching of electricity in the grid and to forecast other macro-economic trends. This paper summarizes and presents auto-regressive techniques/processes as a practical tool in forecasting electricity demand. This paper attempts to model the long-term electricity demand for New Jersey using three different auto-regression models: ARMAX (autoregressive moving average with exogenous variables) model, Vector auto-regressions (VAR) and Bayesian VAR (BVAR). The application of VAR/BVAR to electricity demand forecasting is relatively new and untested. The forecasting performance of each model is assessed using different forecast error metrics. For the given case study, the VAR model produced the best forecast.
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 Data Mining and Machine Learning in Building Energy Analysis written by Frédéric Magoules and published by John Wiley & Sons. This book was released on 2016-01-05 with total page 186 pages. Available in PDF, EPUB and Kindle. Book excerpt: The energy consumption of a building has, in recent years, become a determining factor during its design and construction. With carbon footprints being a growing issue, it is important that buildings be optimized for energy conservation and CO2 reduction. This book therefore presents AI models and optimization techniques related to this application. The authors start with a review of recent models for the prediction of building energy consumption: engineering methods, statistical methods, artificial intelligence methods, ANNs and SVMs in particular. The book then focuses on SVMs, by first applying them to building energy consumption, then presenting the principles and various extensions, and SVR. The authors then move on to RDP, which they use to determine building energy faults through simulation experiments before presenting SVR model reduction methods and the benefits of parallel computing. The book then closes by presenting some of the current research and advancements in the field.
