Modeling And Forecasting Electricity Demand

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Modeling And Forecasting Electricity Demand

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Author : Kevin Berk
language : en
Publisher: Springer Spektrum
Release Date : 2015-01-30

Modeling And Forecasting Electricity Demand written by Kevin Berk and has been published by Springer Spektrum this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-01-30 with Business & Economics categories.

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.

Modeling And Forecasting Electricity Loads And Prices

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Author : Rafal Weron
language : en
Publisher: John Wiley & Sons
Release Date : 2007-01-30

Modeling And Forecasting Electricity Loads And Prices written by Rafal Weron and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-01-30 with Business & Economics categories.

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.

Modeling And Forecasting Electricity Demand

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Author : Kevin Berk
language : en
Publisher: Springer
Release Date : 2015-01-20

Modeling And Forecasting Electricity Demand written by Kevin Berk and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-01-20 with Business & Economics categories.

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.

Modeling And Forecasting Electricity Demand

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Author : Kevin Berk
language : en
Publisher:
Release Date : 2015

Modeling And Forecasting Electricity Demand written by Kevin Berk and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015 with categories.

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. Contents Electricity Market Energy Economy in Enterprises Time Series Analysis A one Factor Model for medium-term Load Forecasting Retail Contract Evaluation and Pricing MATLAB Implementation Target Groups Researchers and students in energy economics or mathematics and statistics with a focus on applications in energy markets Professionals in electricity utilities, energy vendors, risk management The Author Kevin Berk is a Ph.D. student at the Mathematics Department of the University Siegen, Germany. His major research focus is risk management and time series models with applications in energy markets.

Forecasting And Assessing Risk Of Individual Electricity Peaks

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Author : Maria Jacob
language : en
Publisher: Springer Nature
Release Date : 2019-09-25

Forecasting And Assessing Risk Of Individual Electricity Peaks written by Maria Jacob and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-25 with Mathematics categories.

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.

Hybrid Intelligent Technologies In Energy Demand Forecasting

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Author : Wei-Chiang Hong
language : en
Publisher: Springer
Release Date : 2021-01-02

Hybrid Intelligent Technologies In Energy Demand Forecasting written by Wei-Chiang Hong and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-01-02 with Business & Economics categories.

This book is written for researchers and postgraduates who are interested in developing high-accurate energy demand forecasting models that outperform traditional models by hybridizing intelligent technologies. It covers meta-heuristic algorithms, chaotic mapping mechanism, quantum computing mechanism, recurrent mechanisms, phase space reconstruction, and recurrence plot theory. The book clearly illustrates how these intelligent technologies could be hybridized with those traditional forecasting models. This book provides many figures to deonstrate how these hybrid intelligent technologies are being applied to exceed the limitations of existing models.

Short Term Load Forecasting 2019

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Author : Antonio Gabaldón
language : en
Publisher: MDPI
Release Date : 2021-02-26

Short Term Load Forecasting 2019 written by Antonio Gabaldón and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-02-26 with Technology & Engineering categories.

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.

Intelligent Computing And Information Science

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Author : Ran Chen
language : en
Publisher: Springer
Release Date : 2010-12-17

Intelligent Computing And Information Science written by Ran Chen and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010-12-17 with Computers categories.

This two-volume set (CCIS 134 and CCIS 135) constitutes the refereed proceedings of the International Conference on Intelligent Computing and Information Science, ICICIS2011, held in Chongqing, China, in January 2011. The 226 revised full papers presented in both volumes, CCIS 134 and CCIS 135, were carefully reviewed and selected from over 600 initial submissions. The papers provide the reader with a broad overview of the latest advances in the field of intelligent computing and information science.

Forecasting Models Of Electricity Prices

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Author : Javier Contreras
language : en
Publisher: MDPI
Release Date : 2018-04-06

Forecasting Models Of Electricity Prices written by Javier Contreras and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-04-06 with Technology & Engineering categories.

This book is a printed edition of the Special Issue "Forecasting Models of Electricity Prices" that was published in Energies

Data Mining And Machine Learning In Building Energy Analysis

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Author : Frédéric Magoules
language : en
Publisher: John Wiley & Sons
Release Date : 2016-01-05

Data Mining And Machine Learning In Building Energy Analysis written by Frédéric Magoules and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-01-05 with Computers categories.

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.