Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Electrical Engineering | en_US |
dc.contributor.advisor | Xu, Zhao (EE) | - |
dc.creator | Feng, Wenting | - |
dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/8057 | - |
dc.language | English | en_US |
dc.publisher | Hong Kong Polytechnic University | - |
dc.rights | All rights reserved | en_US |
dc.title | Analysis of wind power forecasting technologies | en_US |
dcterms.abstract | In order to get the electricity without pollution in the future, the use of wind power has made significant advances. But in electric power system, the power networks of wind farms integration have become a problem for the commitment unity and power plants control. As everyone knows wind is one of the weather variables that it is difficult to predict. Intermittent in nature, it is hard to forecast the electricity produced of short-term in a wind farm. It is even difficult in general, in the next few hours any advantages get from the wind farms in not best, and may be essential to improve the spinning reserve of power plant. Hence, it is necessary to administrate energy resources and the alternative energy advent, especially wind power, reduced to the use for short-term forecast of advanced tools of wind speed or some same thing, the wind production. It will start from time series prediction method, brief introduce the basic of standards predicted and the model of time sequence. There are two powerful and useful tools to describe an individual time series of the dynamics, called The Auto Regressive Moving Average (ARMA) model and Artificial Neural Networks (ANN) model. ARMA model can only provide point predict, and neural networks provide both point and interval predict by employ the bootstrap method. | en_US |
dcterms.extent | v, 53 pages : illustrations (some color) | en_US |
dcterms.isPartOf | PolyU Electronic Theses | en_US |
dcterms.issued | 2014 | en_US |
dcterms.educationalLevel | All Master | en_US |
dcterms.educationalLevel | M.Sc. | en_US |
dcterms.LCSH | Wind power -- Forecasting. | en_US |
dcterms.LCSH | Wind power plants -- Forecasting. | en_US |
dcterms.LCSH | Hong Kong Polytechnic University -- Dissertations | en_US |
dcterms.accessRights | restricted access | en_US |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
b28183071.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 1.54 MB | Adobe PDF | View/Open |
Copyright Undertaking
As a bona fide Library user, I declare that:
- I will abide by the rules and legal ordinances governing copyright regarding the use of the Database.
- I will use the Database for the purpose of my research or private study only and not for circulation or further reproduction or any other purpose.
- I agree to indemnify and hold the University harmless from and against any loss, damage, cost, liability or expenses arising from copyright infringement or unauthorized usage.
By downloading any item(s) listed above, you acknowledge that you have read and understood the copyright undertaking as stated above, and agree to be bound by all of its terms.
Please use this identifier to cite or link to this item:
https://theses.lib.polyu.edu.hk/handle/200/8057