Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/128216
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Type: Conference paper
Title: Adaptive neuro-surrogate-based optimisation method for wave energy converters placement optimisation
Author: Neshat, M.
Abbasnejad, E.
Shi, Q.
Alexander, B.
Wagner, M.
Citation: Proceedings of the 26th International Conference on Neural Information Processing (ICONIP 2019), as published in Lecture Notes in Computer Science (Neural Information Processing Proceedings, Part II), 2019 / Gedeon, T., Wong, K.W., Lee, M. (ed./s), vol.11954, pp.353-366
Publisher: Springer Nature
Publisher Place: Switzerland
Issue Date: 2019
Series/Report no.: Lecture Notes in Computer Science; 11954
ISBN: 9783030367107
ISSN: 0302-9743
1611-3349
Conference Name: 26th International Conference on Neural Information Processing (ICONIP) (12 Dec 2019 - 15 Dec 2019 : Sydney, Australia)
Statement of
Responsibility: 
Mehdi Neshat, Ehsan Abbasnejad, Qinfeng Shi, Bradley Alexander, and Markus Wagner
Abstract: Installed renewable energy capacity has expanded massively in recent years. Wave energy, with its high capacity factors, has great potential to complement established sources of solar and wind energy. This study explores the problem of optimising the layout of advanced, three-tether wave energy converters in a size-constrained farm in a numerically modelled ocean environment. Simulating and computing the complicated hydrodynamic interactions in wave farms can be computationally costly, which limits optimisation methods to using just a few thousand evaluations. For dealing with this expensive optimisation problem, an adaptive neuro-surrogate optimisation (ANSO) method is proposed that consists of a surrogate Recurrent Neural Network (RNN) model trained with a very limited number of observations. This model is coupled with a fast meta-heuristic optimiser for adjusting the model’s hyper-parameters. The trained model is applied using a greedy local search with a backtracking optimisation strategy. For evaluating the performance of the proposed approach, some of the more popular and successful Evolutionary Algorithms (EAs) are compared in four real wave scenarios (Sydney, Perth, Adelaide and Tasmania). Experimental results show that the adaptive neuro model is competitive with other optimisation methods in terms of total harnessed power output and faster in terms of total computational costs.
Keywords: Evolutionary Algorithms; Local search; Surrogate-based optimisation; Sequential deep learning; Gray Wolf Optimiser; Wave Energy Converters; Renewable energy
Rights: © Springer Nature Switzerland AG 2019
RMID: 1000011545
DOI: 10.1007/978-3-030-36711-4_30
Published version: https://doi.org/10.1007/978-3-030-36711-4
Appears in Collections:Australian Institute for Machine Learning publications

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