Modeling and Forecasting Electricity Demand A Risk Management Perspective / by Kevin Berk.

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...

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Bibliographic Details
Main Author: Berk, Kevin (Author)
Corporate Author: SpringerLink (Online service)
Format: eBook
Language:English
Published: Wiesbaden : Springer Fachmedien Wiesbaden : Imprint: Springer Spektrum, 2015.
Edition:1st ed. 2015.
Series:BestMasters,
Springer eBook Collection.
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Online Access:Click to view e-book
Holy Cross Note:Loaded electronically.
Electronic access restricted to members of the Holy Cross Community.
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Summary: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.
Physical Description:XVI, 115 p. 39 illus. online resource.
ISBN:9783658086695
ISSN:2625-3577
DOI:10.1007/978-3-658-08669-5