Wind power forecasting using wavelet transforms and neural networks with tapped delay

Sumit Saroha; S. K. Aggarwal

doi:10.17775/CSEEJPES.2016.00970

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Wind power forecasting using wavelet transforms and neural networks with tapped delay

更新时间：2025-12-18

- Wind power forecasting using wavelet transforms and neural networks with tapped delay
- Wind power forecasting using wavelet transforms and neural networks with tapped delay
- 中国电机工程学会电力与能源系统学报(英文) 2018年4卷第2期页码：197-209
- 作者机构：
  
  1. SRM University, Kattankulathur, Tamil Nadu,IN
  2. Thapar University, Patiala, Punjab,IN
- 作者简介：
- 基金信息：
- DOI：10.17775/CSEEJPES.2016.00970
  中图分类号：
- 纸质出版：2018
- 稿件说明：
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Sumit Saroha, S. K. Aggarwal. Wind power forecasting using wavelet transforms and neural networks with tapped delay[J]. 中国电机工程学会电力与能源系统学报(英文), 2018,4(2):197-209.

Sumit Saroha, S. K. Aggarwal. Wind power forecasting using wavelet transforms and neural networks with tapped delay[J]. CSEE Journal of Power and Energy Systems, 2018, 4(2): 197-209.
Sumit Saroha, S. K. Aggarwal. Wind power forecasting using wavelet transforms and neural networks with tapped delay[J]. 中国电机工程学会电力与能源系统学报(英文), 2018,4(2):197-209. DOI： 10.17775/CSEEJPES.2016.00970.

Sumit Saroha, S. K. Aggarwal. Wind power forecasting using wavelet transforms and neural networks with tapped delay[J]. CSEE Journal of Power and Energy Systems, 2018, 4(2): 197-209. DOI： 10.17775/CSEEJPES.2016.00970.

摘要

Abstract

With an objective to improve wind power estimation accuracy and reliability

this paper presents Linear Neural Networks with Tapped Delay (LNNTD) in combination with wavelet transform (WT) for probabilistic wind power forecasting in a time series framework. For comparison purposes

results of the proposed model are compared with the benchmark model

different neural networks and WT based models considering performance indices such as accuracy

execution time and R

statistic. For the reliability and proper validation of the proposed model

this paper highlights the probabilistic forecast attributes at different skill tests. The historical data of the Ontario Electricity Market (OEM) for the period 2011-2014 were used and tested for two years from November 2012 to October 2014 with one month moving window considering all seasonal aspects. The experimental results clearly show that the results of the proposed model have been found to be better than others.

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