A fuzzy group forecasting model based on BPNN for wind power output

Qian Zhang; Kin Keung Lai; Dongxiao Niu; Qiang Wang

doi:10.1109/BIFE.2012.9

A fuzzy group forecasting model based on BPNN for wind power output

Qian Zhang, Kin Keung Lai, Dongxiao Niu, Qiang Wang

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

2 Citations (Scopus)

Abstract

Many forecasting models have been developed for forecasting wind farm electricity output. In most situations, performance of models is problem-dependent. Thus, it is difficult for forecasters to choose the right technique for each unique situation. In order to overcome this problem, this paper integrates multiple models into an aggregated model to obtain further performance improvement. Firstly, three groups of BPNN forecasting models are designed, i.e. univariate BPNN models, the hybrid model of ARIMA and BPNN and the multivariate model. Each group of the models can be regarded as an expert in forecasting, and then the fuzzy theory is used to combine all these forecasting results into the final answer. Results show that this group forecasting model performs well in terms of accuracy and consistency. © 2012 IEEE.

Original language	English
Title of host publication	Proceedings of the 2012 5th International Conference on Business Intelligence and Financial Engineering, BIFE 2012
Pages	1-5
DOIs	https://doi.org/10.1109/BIFE.2012.9
Publication status	Published - 2012
Event	2012 5th International Conference on Business Intelligence and Financial Engineering, BIFE 2012 - Lanzhou, Gansu, China Duration: 18 Aug 2012 → 21 Aug 2012

Conference

Conference	2012 5th International Conference on Business Intelligence and Financial Engineering, BIFE 2012
Place	China
City	Lanzhou, Gansu
Period	18/08/12 → 21/08/12

Research Keywords

ARIMA
BPNN
Fuzzy group
Wind power forecasting

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/BIFE.2012.9

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@inproceedings{08c62f6fdb044fad9676d4b662d69d28,

title = "A fuzzy group forecasting model based on BPNN for wind power output",

abstract = "Many forecasting models have been developed for forecasting wind farm electricity output. In most situations, performance of models is problem-dependent. Thus, it is difficult for forecasters to choose the right technique for each unique situation. In order to overcome this problem, this paper integrates multiple models into an aggregated model to obtain further performance improvement. Firstly, three groups of BPNN forecasting models are designed, i.e. univariate BPNN models, the hybrid model of ARIMA and BPNN and the multivariate model. Each group of the models can be regarded as an expert in forecasting, and then the fuzzy theory is used to combine all these forecasting results into the final answer. Results show that this group forecasting model performs well in terms of accuracy and consistency. {\textcopyright} 2012 IEEE.",

keywords = "ARIMA, BPNN, Fuzzy group, Wind power forecasting",

author = "Qian Zhang and Lai, {Kin Keung} and Dongxiao Niu and Qiang Wang",

year = "2012",

doi = "10.1109/BIFE.2012.9",

language = "English",

isbn = "9780769547503",

pages = "1--5",

booktitle = "Proceedings of the 2012 5th International Conference on Business Intelligence and Financial Engineering, BIFE 2012",

note = "2012 5th International Conference on Business Intelligence and Financial Engineering, BIFE 2012 ; Conference date: 18-08-2012 Through 21-08-2012",

}

Zhang, Q, Lai, KK, Niu, D & Wang, Q 2012, A fuzzy group forecasting model based on BPNN for wind power output. in Proceedings of the 2012 5th International Conference on Business Intelligence and Financial Engineering, BIFE 2012., 6305067, pp. 1-5, 2012 5th International Conference on Business Intelligence and Financial Engineering, BIFE 2012, Lanzhou, Gansu, China, 18/08/12. https://doi.org/10.1109/BIFE.2012.9

A fuzzy group forecasting model based on BPNN for wind power output. / Zhang, Qian; Lai, Kin Keung; Niu, Dongxiao et al.
Proceedings of the 2012 5th International Conference on Business Intelligence and Financial Engineering, BIFE 2012. 2012. p. 1-5 6305067.

Research output: Chapters, Conference Papers, Creative and Literary Works › RGC 32 - Refereed conference paper (with host publication) › peer-review

TY - GEN

T1 - A fuzzy group forecasting model based on BPNN for wind power output

AU - Zhang, Qian

AU - Lai, Kin Keung

AU - Niu, Dongxiao

AU - Wang, Qiang

PY - 2012

Y1 - 2012

N2 - Many forecasting models have been developed for forecasting wind farm electricity output. In most situations, performance of models is problem-dependent. Thus, it is difficult for forecasters to choose the right technique for each unique situation. In order to overcome this problem, this paper integrates multiple models into an aggregated model to obtain further performance improvement. Firstly, three groups of BPNN forecasting models are designed, i.e. univariate BPNN models, the hybrid model of ARIMA and BPNN and the multivariate model. Each group of the models can be regarded as an expert in forecasting, and then the fuzzy theory is used to combine all these forecasting results into the final answer. Results show that this group forecasting model performs well in terms of accuracy and consistency. © 2012 IEEE.

AB - Many forecasting models have been developed for forecasting wind farm electricity output. In most situations, performance of models is problem-dependent. Thus, it is difficult for forecasters to choose the right technique for each unique situation. In order to overcome this problem, this paper integrates multiple models into an aggregated model to obtain further performance improvement. Firstly, three groups of BPNN forecasting models are designed, i.e. univariate BPNN models, the hybrid model of ARIMA and BPNN and the multivariate model. Each group of the models can be regarded as an expert in forecasting, and then the fuzzy theory is used to combine all these forecasting results into the final answer. Results show that this group forecasting model performs well in terms of accuracy and consistency. © 2012 IEEE.

KW - ARIMA

KW - BPNN

KW - Fuzzy group

KW - Wind power forecasting

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U2 - 10.1109/BIFE.2012.9

DO - 10.1109/BIFE.2012.9

M3 - RGC 32 - Refereed conference paper (with host publication)

SN - 9780769547503

SP - 1

EP - 5

BT - Proceedings of the 2012 5th International Conference on Business Intelligence and Financial Engineering, BIFE 2012

T2 - 2012 5th International Conference on Business Intelligence and Financial Engineering, BIFE 2012

Y2 - 18 August 2012 through 21 August 2012

ER -

A fuzzy group forecasting model based on BPNN for wind power output

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Research Keywords

UN SDGs

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