Email this article Backpropagation Neural Network (BPNN) Algorithm for Predicting Wind Speed Patterns in East Nusa Tenggara
Abstract
The Paris agreement compels all countries to make major contributions to the zero-emission scheme, a legally binding international treaty on climate change. This fulfilment must be supported by technological developments towards Society 5.0, forcing every country to develop renewable energy (clean energy) on a large scale. One of the renewable energies with the highest efficiency is wind power generation. Its construction requires a large cost, and the best location must consider the high wind speed. East Nusa Tenggara Province is one of the locations in the border area with insufficient electricity. The choice of location was supported by military operations in guarding the border which required a lot of energy. Therefore, it is necessary to predict wind speed patterns based on historical data from the database so that wind power plants can be realized. One of the best methods for long-term prediction of wind speed is the backpropagation neural network (BPPN) method. Wind speed data was used from January 2003 to December 2020 with a total of 216 data sets obtained from NASA. It should be noted that January 2003 to December 2010 data is positioned as input data, while training target data is from January 2011-December 2015. Validation data is determined from January 2016-December 2020. The best predictive architecture model is 8-11-5- 5, learning rate is 0.4 and epoch is 20,000. Prediction accuracy is very good with a mean square error (MSE) value of 0.007634 and a mean absolute percentage error (MAPE) of 11.62783. The highest wind speed was shown in February 2018 as 10.75 m/s.
Citation: Gunawan, A., Thamrin, S., Kuntjoro, Y. D., and Idris, A. M. (2022). Backpropagation Neural Network (BPNN) Algorithm for Predicting Wind Speed Patterns in East Nusa Tenggara. Trends in Renewable Energy, 8, 107-118. DOI: 10.17737/tre.2022.8.2.00143
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DOI: http://dx.doi.org/10.17737/tre.2022.8.2.00143
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Copyright (c) 2022 Andri Gunawan, Suyono Thamrin, Yanif Dwi Kuntjoro, Abdi Manab Idris
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This work is licensed under a Creative Commons Attribution 4.0 License.Copyright @2014-2024 Trends in Renewable Energy (ISSN: 2376-2136, online ISSN: 2376-2144)