Probabilistic wind power forecasting using a novel hybrid intelligent method

被引:26
作者
Afshari-Igder, Moseyeb [1 ]
Niknam, Taher [1 ]
Khooban, Mohammad-Hassan [1 ]
机构
[1] Shiraz Univ Technol, Dept Elect Engn, Shiraz, Iran
关键词
Neural network; Improved krill herd optimization algorithm; Bootstrap; Wind power forecast; Prediction intervals; EXTREME LEARNING-MACHINE; SHORT-TERM LOAD; ELECTRICITY PRICE; INTERVAL; ELM;
D O I
10.1007/s00521-016-2703-z
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
As a consequence of increasing wind power penetration level, it will be a big challenge to control and operate the power system because of the inherent uncertainty of the wind energy. One of the ways to deal with the wind power variability is to predict it accurately and reliably. The traditional point forecasting-based technique cannot notably solve the uncertainty in power system operation. In order to compute the probabilistic forecasting, which yields information on the uncertainty of wind power, a novel hybrid intelligent method that incorporates the wavelet transform, neural network (NN), and improved krill herd optimization algorithm (IKHOA), is used in this paper. Also, the extreme learning machine is exerted to train NN and calculates point forecasts, and IKHOA is applied to forecast the noise variance. The robust method called bootstrap is regarded to create prediction intervals and calculate the model uncertainty. The efficiency of proposed forecasting engine is evaluated by usage of wind power data from the Alberta, Canada.
引用
收藏
页码:473 / 485
页数:13
相关论文
共 30 条
[1]  
[Anonymous], 1993, An introduction to the bootstrap
[2]  
[Anonymous], NEURAL COMPUT APPL
[3]   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
[4]   Time-adaptive quantile-copula for wind power probabilistic forecasting [J].
Bessa, Ricardo J. ;
Miranda, V. ;
Botterud, A. ;
Zhou, Z. ;
Wang, J. .
RENEWABLE ENERGY, 2012, 40 (01) :29-39
[5]   AWNN-Assisted Wind Power Forecasting Using Feed-Forward Neural Network [J].
Bhaskar, Kanna ;
Singh, S. N. .
IEEE TRANSACTIONS ON SUSTAINABLE ENERGY, 2012, 3 (02) :306-315
[6]   Day-ahead electricity price forecasting using the wavelet transform and ARIMA models [J].
Conejo, AJ ;
Plazas, MA ;
Espínola, R ;
Molina, AB .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2005, 20 (02) :1035-1042
[7]   A review on the young history of the wind power short-term prediction [J].
Costa, Alexandre ;
Crespo, Antonio ;
Navarro, Jorge ;
Lizcano, Gil ;
Madsen, Henrik ;
Feitosa, Everaldo .
RENEWABLE & SUSTAINABLE ENERGY REVIEWS, 2008, 12 (06) :1725-1744
[8]   Confidence intervals for neural network based short-term load forecasting [J].
da Silva, AP ;
Moulin, LS .
IEEE TRANSACTIONS ON POWER SYSTEMS, 2000, 15 (04) :1191-1196
[9]   Very-Short-Term Probabilistic Wind Power Forecasts by Sparse Vector Autoregression [J].
Dowell, Jethro ;
Pinson, Pierre .
IEEE TRANSACTIONS ON SMART GRID, 2016, 7 (02) :763-770
[10]   Krill herd: A new bio-inspired optimization algorithm [J].
Gandomi, Amir Hossein ;
Alavi, Amir Hossein .
COMMUNICATIONS IN NONLINEAR SCIENCE AND NUMERICAL SIMULATION, 2012, 17 (12) :4831-4845