Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/334524
Title: Hybrid machine learning neural network architectural models for forecasting of wind speed in renewable energy applications
Researcher: Vinoth Kumar, T
Guide(s): Deeba, K
Keywords: Machine Learning
Neural Network
Renewable Energy
University: Anna University
Completed Date: 2020
Abstract: Wind energy is a non-linear entity due to its relationship with wind speed and based on the stochastic nature of the natural wind source. When an error occurs during the forecasting of the wind speed, this results in an unexpected error factor in the generation of power from wind energy sources. Thus, it is highly important to predict the wind speed in a most possible accurate manner so as to avoid the presence of errors in wind energy generation. Basically, the development of neural network architectural models have paved a way for better prediction accuracy since it learns and get adapted with respect to the past experiences. Due to which, the applicability of neural network architectures shall enhance wind speed prediction to a higher rate and this may permit the grid sectors to operate in an effective manner to meet the demands of the end user customers. Considering the factors, this thesis has modelled novel techniques that are scalable, effective and plausible models and the summary of findings with significant contributions are presented here. Firstly, a new wavelet based ELMAN JORDAN neural network is modelled with its architectural design for forecasting the wind speed for the considered locations of the wind farm. Basically, ELMAN JORDAN neural network design is a recurrent neural network model and the concept of wavelet functions are introduced into this network for better convergence and to attain minimal square error. The newly modelled neural network is tuned for its weights and bias parameters employing the proposed hybrid particle swarm optimization (PSO) algorithm and ant lion optimization (ALO) algorithm. The advantages of both particle swarm optimization algorithm and ant lion optimization algorithm are hybridized effectively to construct this iv iv hybrid PSO-ALO algorithm. Numerical simulations are carried out employing the proposed hybrid optimization based neural network models for the considered wind farm datasets and the attained results prove the efficacy and effectiveness of the pro
Pagination: xxx,220 p.
URI: http://hdl.handle.net/10603/334524
Appears in Departments:Faculty of Electrical Engineering

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02_certificates.pdf176.54 kBAdobe PDFView/Open
03_vivaproceedings.pdf475.35 kBAdobe PDFView/Open
04_bonafidecertificate.pdf223.22 kBAdobe PDFView/Open
05_abstracts.pdf146.74 kBAdobe PDFView/Open
06_acknowledgements.pdf188.9 kBAdobe PDFView/Open
07_contents.pdf222.41 kBAdobe PDFView/Open
08_listoftables.pdf173.36 kBAdobe PDFView/Open
09_listoffigures.pdf145.43 kBAdobe PDFView/Open
10_listofabbreviations.pdf249.89 kBAdobe PDFView/Open
11_chapter1.pdf1.07 MBAdobe PDFView/Open
12_chapter2.pdf462.6 kBAdobe PDFView/Open
13_chapter3.pdf354.89 kBAdobe PDFView/Open
14_chapter4.pdf1.21 MBAdobe PDFView/Open
15_chapter5.pdf429.91 kBAdobe PDFView/Open
16_chapter6.pdf351.23 kBAdobe PDFView/Open
17_conclusion.pdf178.36 kBAdobe PDFView/Open
18_appendices.pdf253.52 kBAdobe PDFView/Open
19_references.pdf1.01 MBAdobe PDFView/Open
20_listofpublications.pdf138.73 kBAdobe PDFView/Open
80_recommendation.pdf166.83 kBAdobe PDFView/Open
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