Markov chain modeling for very-short-term wind power forecasting

被引：124

作者：

Carpinone, A. ^{[1
]}

Giorgio, M. ^{[1
]}

Langella, R. ^{[1
]}

Testa, A. ^{[1
]}

机构：

[1] Univ Naples 2, Dept Ind & Informat Engn, I-81031 Aversa, CE, Italy

来源：

ELECTRIC POWER SYSTEMS RESEARCH | 2015年 / 122卷

关键词：

Wind power; Markov chain models; Point forecast; Interval forecasts; Very-short-term forecasting; Statistical methods; QUANTILE REGRESSION; TIME-SERIES; GENERATION; PREDICTION; INTERVALS;

D O I：

10.1016/j.epsr.2014.12.025

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

A Wind power forecasting method based on the use of discrete time Markov chain models is developed starting from real wind power time series data. It allows to directly obtain in an easy way an estimate of the wind power distributions on a very short-term horizon, without requiring restrictive assumptions on wind power probability distribution. First and Second Order Markov Chain Model are analytically described. Finally, the application of the proposed method is illustrated with reference to a set of real data. (C) 2015 The Authors. Published by Elsevier B.V.

引用

页码：152 / 158

页数：7

共 40 条

[1]

Aggarwal S.K., 2013, INT J ENERGY SCI IJE, V3, P1

[2]

[Anonymous], J BUS ECON STAT

[3] Trading wind generation in short term energy markets [J].

Bathurst, GN ;

Weatherill, J ;

Strbac, G .

IEEE TRANSACTIONS ON POWER SYSTEMS, 2002, 17 (03) :782-789

[4] Time Adaptive Conditional Kernel Density Estimation for Wind Power Forecasting [J].

Bessa, Ricardo J. ;

Miranda, Vladimiro ;

Botterud, Audun ;

Wang, Jianhui ;

Constantinescu, Emil M. .

IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, 2012, 3 (04) :660-669

[5]

Bhat U.N., 1984, ELEMENTS APPL STOCHA

[6] Very short-term wind power forecasting with neural networks and adaptive Bayesian learning [J].

Blonbou, Ruddy .

RENEWABLE ENERGY, 2011, 36 (03) :1118-1124

[7]

Box G.E.P., 1976, Time Series Analysis: Forecasting and Control

[8] Probabilistic wind power forecasts using local quantile regression [J].

Bremnes, JB .

WIND ENERGY, 2004, 7 (01) :47-54

[9]

Buizza R., 2009, CHAOS WEATHER PREDIC

[10]

CABEZON D, 2004, P GLOB WIND POW 2004

← 1 2 3 4 →