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|Title:||Adaptive neuro-surrogate-based optimisation method for wave energy converters placement optimisation|
|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|
|Series/Report no.:||Lecture Notes in Computer Science; 11954|
|Conference Name:||26th International Conference on Neural Information Processing (ICONIP) (12 Dec 2019 - 15 Dec 2019 : Sydney, Australia)|
|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|
|Appears in Collections:||Australian Institute for Machine Learning publications|
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