Probability density function and prediction of wind power variations

被引：0

作者：

Gao, Qiuli ^{[1
]}

Yuan, Chidao ^{[1
]}

Zuo, Zhengren ^{[1
]}

Feng, Yuan ^{[2
]}

Gai, Cihang ^{[3
]}

机构：

[1] Financial Engineering, Jinan University, Zhuhai, China

[2] Institute of Translation Study, Jinan University, Zhuhai, China

[3] Institute of Accounting (CPA), Jinan University, Zhuhai, China

来源：

Journal of Information and Computational Science | 2014年 / 11卷 / 13期

关键词：

Neural networks - Probability density function - Pareto principle;

D O I：

10.12733/jics20104306

中图分类号：

学科分类号：

摘要：

Variation is an intrinsic property of wind power. It has been proved that generalized Pareto distribution is suitable to identify the probability distribution of wind power variations after mass field measurements. And analysis shows that distribution of wind power is leptokurtic and rightward skewed. Based on its distribution character, this paper first adopts the wavelet analysis technique to denoise the probability distribution of wind power variation and then applies BP Artificial Neural Networks to achieve short-term rolling forecast of the variation whose precision is up to 76.27%. Copyright © 2014 Binary Information Press.

引用

页码：4499 / 4505

共 50 条

[1] The Probability Density Function Based Neuro-Fuzzy Wind Power Prediction With Global Convergence
Li, Jianfang
Jia, Li
Peng, Daogang
Hou, Rui
[J]. IEEE Transactions on Industry Applications, 2024, 60 (06) : 8464 - 8481
[2] Investigation of wind power density distribution using Rayleigh probability density function
Paraschiv, Lizica-Simona
Paraschiv, Spiru
Ion, V. Ion
[J]. TECHNOLOGIES AND MATERIALS FOR RENEWABLE ENERGY, ENVIRONMENT AND SUSTAINABILITY (TMREES), 2019, 157 : 1546 - 1552
[3] Investigation of wind power density distribution using Rayleigh probability density function
Paraschiv, Lizica-Simona
Paraschiv, Spiru
Ion, Ion V.
[J]. Energy Procedia, 2019, 157 : 1546 - 1552
[4] Wind Power Density Estimation Using Rayleigh Probability Distribution Function
Murthy, K. S. R.
Rahi, O. P.
[J]. APPLICATIONS OF ARTIFICIAL INTELLIGENCE TECHNIQUES IN ENGINEERING, SIGMA 2018, VOL 1, 2019, 698 : 265 - 275
[5] A statistical method based on the ensemble probability density function for the prediction of "Wind Days"
Tateo, A.
Miglietta, M. M.
Fedele, F.
Menegotto, M.
Pollice, A.
Bellotti, R.
[J]. ATMOSPHERIC RESEARCH, 2019, 216 : 106 - 116
[6] Accuracy of wind speed forecasting based on joint probability prediction of the parameters of the Weibull probability density function
Majid, Amir Abdul
[J]. FRONTIERS IN ENERGY RESEARCH, 2023, 11
[7] A New Modeling Approach for the Probability Density Distribution Function of Wind power Fluctuation
Wang, Lingzhi
Liu, Jun
Qian, Fucai
[J]. SUSTAINABILITY, 2019, 11 (19)
[8] Probability distribution function of wind power variations based on the MSTV-EGARCH model
[J]. Liu, X., 1600, Asian Network for Scientific Information (12):
[9] Probability prediction of wind power based on QR-NFGLSTM and kernel density estimation
Wang, Xiaodong
Ju, Bangguo
Liu, Yingming
Zang, Tonglin
[J]. Taiyangneng Xuebao/Acta Energiae Solaris Sinica, 2022, 43 (02): : 479 - 485
[10] Probability density function of day-ahead wind power forecast errors based on power curves of wind farms
[J]. Ding, H. (dinghj11@mails.tsinghua.edu.cn), 1600, Chinese Society for Electrical Engineering (33):

← 1 2 3 4 5 →