Development and Testing of Artificial Neural Network with Backpropagation Algorithm to Predict the Power Ratio of Savonius Wind Turbine
- 10.2991/apr.k.220503.018How to use a DOI?
- ANN; rotor; Savonius; wind
A power ratio of Savonius as a vertical wind type has been predicted using an artificial neural network (ANN) with a backpropagation algorithm. The ANN is a method for processing information that is adapted from a biological neuron. This method is developed from the principle of a human being’s brain which is consist of input and output neurons. The procedure in this method is conducted using the initial input as the power ratio target to predict the next value as the output target. This prediction is employed to know the blade characteristic and its effect on the power. The ANN architecture is multi-layers with a backpropagation algorithm. The layers are input, hidden, and output. The learning rule is consisting of the forward-propagation, backward-propagation, and weight-update with the number of neurons being 2-5-1, respectively. The result of this development and testing shows that the optimum values for momentum and learning rate are 0.90 and 0.01, respectively. The result has been tested by comparing the output and input with an error of approximately 0.65%. This result indicates that the ANN method with backpropagation algorithm is prospective to predict the power ratio of various blades of Savonius wind turbine.
- © 2022 The Authors. Published by Atlantis Press International B.V.
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Cite this article
TY - CONF AU - Jamrud Aminuddin AU - Bilalodin Bilalodin PY - 2022 DA - 2022/05/25 TI - Development and Testing of Artificial Neural Network with Backpropagation Algorithm to Predict the Power Ratio of Savonius Wind Turbine BT - Proceedings of the Soedirman International Conference on Mathematics and Applied Sciences (SICOMAS 2021) PB - Atlantis Press SP - 84 EP - 90 SN - 2352-541X UR - https://doi.org/10.2991/apr.k.220503.018 DO - 10.2991/apr.k.220503.018 ID - Aminuddin2022 ER -