DocumentCode :
88399
Title :
A Survey on Electric Power Demand Forecasting: Future Trends in Smart Grids, Microgrids and Smart Buildings
Author :
Hernandez, L. ; Baladron, Carlos ; Aguiar, Javier M. ; Carro, Belen ; Sanchez-Esguevillas, Antonio J. ; Lloret, Jaime ; Massana, Joaquim
Author_Institution :
Centro de Investig. Energeticas, Medioambientales y Tecnol. (CIEMAT), Salida, CA, USA
Volume :
16
Issue :
3
fYear :
2014
fDate :
Third Quarter 2014
Firstpage :
1460
Lastpage :
1495
Abstract :
Recently there has been a significant proliferation in the use of forecasting techniques, mainly due to the increased availability and power of computation systems and, in particular, to the usage of personal computers. This is also true for power network systems, where energy demand forecasting has been an important field in order to allow generation planning and adaptation. Apart from the quantitative progression, there has also been a change in the type of models proposed and used. In the `70s, the usage of non-linear techniques was generally not popular among scientists and engineers. However, in the last two decades they have become very important techniques in solving complex problems which would be very difficult to tackle otherwise. With the recent emergence of smart grids, new environments have appeared capable of integrating demand, generation, and storage. These employ intelligent and adaptive elements that require more advanced techniques for accurate and precise demand and generation forecasting in order to work optimally. This review discusses the most relevant studies on electric demand prediction over the last 40 years, and presents the different models used as well as the future trends. Additionally, it analyzes the latest studies on demand forecasting in the future environments that emerge from the usage of smart grids.
Keywords :
buildings (structures); demand forecasting; distributed power generation; power distribution planning; smart power grids; computation systems; electric demand prediction; electric power demand forecasting; energy demand forecasting; generation planning; microgrids; nonlinear techniques; personal computers; power network systems; quantitative progression; smart buildings; smart grids; Biological system modeling; Demand forecasting; Load modeling; Microgrids; Predictive models; Smart grids; Electric demand forecasting; microgrid; short-term load forecasting; smart building; smart grid;
fLanguage :
English
Journal_Title :
Communications Surveys & Tutorials, IEEE
Publisher :
ieee
ISSN :
1553-877X
Type :
jour
DOI :
10.1109/SURV.2014.032014.00094
Filename :
6803101
Link To Document :
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