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[null,"1.","Record Nr.",null,"UNINA9910598040703321"]
[null,null,"Autore",null,"Contreras Javier"]
[null,null,"Titolo",null,"Forecasting Models of Electricity Prices / / Javier Contreras"]
[null,null,"Pubbl/distr/stampa",null,"Basel, Switzerland : , : MDPI - Multidisciplinary Digital Publishing Institute, , 2017"]
[null,null,"Descrizione fisica",null,"1 online resource (258 pages)"]
[null,null,"Disciplina",null,"303.49"]
[null,null,"Soggetti",null,"Forecasting"]
[{"$nulls":4},"Electricity"]
[{"$nulls":4},"Prices"]
[null,null,"Lingua di pubblicazione",null,"Inglese"]
[null,null,"Formato",null,"Materiale a stampa"]
[null,null,null,"Livello bibliografico","Monografia"]
[null,null,null,"Sommario/riassunto","The electric power industry has been in transition, from a centralized, towards a deregulated, production scheme since the early 1980s. Previous centralized schemes were based on electricity tariffs that were paid by the customers as a function of the aggregate cost of production. In the new unbundled scheme, price forecasting has become an important tool for electric companies and customers to decide on their production offers and demand bids and for regulators to characterize the degree of competition of the market. Electricity prices have unique features that are not observed in other markets, such as weekly and daily seasonalities, on-peak vs. off-peak hours, price spikes, etc. The fact that electricity is not easily storable and the requirement of meeting the demand at all times makes the development of forecasting techniques a challenging issue. This Special Issue will include the most important forecasting techniques applied to the forecasting of electricity prices, such as: Statistical time series models: auto regression models, GARCH, Fourier and wavelet transform models, Fundamental or structural econometric models, Regime-switching models: Markov, jump diffusion, Multi-agent and game theoretic equilibrium models: Nash-Cournot, supply function equilibrium, agent-based methods, etc., Artificial intelligence models: Neural networks, fuzzy logic, support vector machines, etc. In this Special Issue, we invite submissions exploring cutting-edge research and recent advances in the field of electricity price forecasting."]