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
Keywords
Full Text:
FULL TEXT (PDF)References
Mujiono, M. N. (2021). The Shifting Role of Accountants in the Era of Digital Disruption. International Journal of Multidisciplinary: Applied Business and Education Research, 2(11), 1259-1274.
Önday, Ö. (2020). Society 5.0-its historical logic and its structural development. Journal of Scientific Reports, 2(1), 32-42.
Achmad, W. (2021). Citizen and Netizen Society: The Meaning of Social Change From a Technology Point of View. Jurnal Mantik, 5(3), 1564-1570.
Oshiro, K., Masui, T., and Kainuma, M. (2018). Transformation of Japan's energy system to attain net-zero emission by 2050. Carbon Management, 9(5), 493-501.
Cassarino, T. G., and Barrett, M. (2022). Meeting UK heat demands in zero emission renewable energy systems using storage and interconnectors. Applied Energy, 306, 118051.
Washburn, C., and Pablo-Romero, M. (2019). Measures to promote renewable energies for electricity generation in Latin American countries. Energy Policy, 128, 212-222.
Albadi, M. H., and El-Saadany, E. F. (2010). Overview of wind power intermittency impacts on power systems. Electric power systems research, 80(6), 627-632.
Malik, N. Y. (2019). Visual landscape analysis of coastal tourism potential in Geopark Ciletuh-Palabuhanratu Indonesia. Scientific Bulletin" Mircea cel Batran" Naval Academy, 22(2), 46A-52.
Körner, C. (2007). The use of ‘altitude’in ecological research. Trends in Ecology & Evolution, 22(11), 569-574.
Paulus, C. A., Azmanajaya, E., Pellokila, M. R., and Paranoan, N. (2020, February). Prospective strategies for sustainable local economic development in support of the SDGs’ goals “inclusive and sustainable economic growth” in the border region of Indonesia–Timor Leste, Belu Regency, East Nusa Tenggara Province, Indonesia. In Journal of Physics: Conference Series (Vol. 1464, No. 1, p. 012053). IOP Publishing.
Kotzé, L. J. (2014). Human rights and the environment in the Anthropocene. The Anthropocene Review, 1(3), 252-275.
Vadivelan, N., Taware, M. S., Chakravarthi, M. R. R., Palagan, C. A., and Gupta, S. (2021). A border surveillance system to sense terrorist outbreaks. Computers & Electrical Engineering, 94, 107355.
Kusiak, A., Zhang, Z., and Verma, A. (2013). Prediction, operations, and condition monitoring in wind energy. Energy, 60, 1-12.
Dai, J., Yang, X., Hu, W., Wen, L., and Tan, Y. (2018). Effect investigation of yaw on wind turbine performance based on SCADA data. Energy, 149, 684-696.
Rasmussen, D. J., Holloway, T., and Nemet, G. F. (2011). Opportunities and challenges in assessing climate change impacts on wind energy—a critical comparison of wind speed projections in California. Environmental Research Letters, 6(2), 024008.
Brahimi, T. (2019). Using artificial intelligence to predict wind speed for energy application in Saudi Arabia. Energies, 12(24), 4669.
Khosravi, A., Machado, L., and Nunes, R. O. (2018). Time-series prediction of wind speed using machine learning algorithms: A case study Osorio wind farm, Brazil. Applied Energy, 224, 550-566.
TASDEMİR, S., YANİKTEPE, B., and burak GUHER, A. (2018). The effect on the wind power performance of different normalization methods by using multilayer feed-forward backpropagation neural network. International Journal of Energy Applications and Technologies, 5(3), 131-139.
Kulkarni, P. A., Dhoble, A. S., and Padole, P. M. (2019). Deep neural network-based wind speed forecasting and fatigue analysis of a large composite wind turbine blade. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 233(8), 2794-2812.
Sun, W. and Wang, Y. (2018). Short-term wind speed forecasting based on fast ensemble empirical mode decomposition, phase space reconstruction, sample entropy and improved back-propagation neural network. Energy conversion and Management, 157, 1-12.
Mia, M. M. A., Biswas, S. K., Urmi, M. C., and Siddique, A. (2015). An algorithm for training multilayer perceptron (MLP) for Image reconstruction using neural network without overfitting. International Journal of Scientific & Technology Research, 4(02), 271-275.
Kim, S. E. and Seo, I. W. (2015). Artificial Neural Network ensemble modeling with conjunctive data clustering for water quality prediction in rivers. Journal of Hydro-Environment Research, 9(3), 325-339.
Mislan, M., Haviluddin, H., Hardwinarto, S., Sumaryono, S., and Aipassa, M. (2015, August). Rainfall monthly prediction based on artificial neural network: a case study in Tenggarong Station, East Kalimantan-Indonesia. The International Conference on Computer Science and Computational Intelligence (ICCSCI 2015)-Procedia Computer Science 59.
Karsoliya, S. (2012). Approximating number of hidden layer neurons in multiple hidden layer BPNN architecture. International Journal of Engineering Trends and Technology, 3(6), 714-717.
Sharma, S., Sharma, S., and Athaiya, A. (2017). Activation functions in neural networks. Towards Data Science, 6(12), 310-316.
Asaad, R. R. and Ali, R. I. (2019). Back Propagation Neural Network (BPNN) and sigmoid activation function in multi-layer networks. Academic Journal of Nawroz University, 8(4), 216-221.
Alfred, R. (2015, October). A genetic-based backpropagation neural network for forecasting in time-series data. In 2015 International Conference on Science in Information Technology (ICSITech) (pp. 158-163). IEEE.
Bai, Y., Li, Y., Wang, X., Xie, J., and Li, C. (2016). Air pollutants concentrations forecasting using back propagation neural network based on wavelet decomposition with meteorological conditions. Atmospheric Pollution Research, 7(3), 557-566.
Eesa, A. S. and Arabo, W. K. (2017). A normalization methods for backpropagation: a comparative study. Science Journal of University of Zakho, 5(4), 319-323.
Wang, H. S., Wang, Y. N., and Wang, Y. C. (2013). Cost estimation of plastic injection molding parts through integration of PSO and BP neural network. Expert Systems with Applications, 40(2), 418-428.
Rozycki, P., Kolbusz, J., Krzos, G., and Wilamowski, B. M. (2019, April). Approximation-based Estimation of Learning Rate for Error Back Propagation Algorithm. In 2019 IEEE 23rd International Conference on Intelligent Engineering Systems (INES) (pp. 000065-000070). IEEE.
Wanto, A., Zarlis, M., and Hartama, D. (2017, December). Analysis of Artificial Neural Network Backpropagation Using Conjugate Gradient Fletcher Reeves in the Predicting Process. In Journal of Physics: Conference Series (Vol. 930, No. 1, p. 012018). IOP Publishing.
Okumus, I. and Dinler, A. (2016). Current status of wind energy forecasting and a hybrid method for hourly predictions. Energy Conversion and Management, 123, 362-371.
Saputra, W., Hardinata, J. T., and Wanto, A. (2020). Resilient method in determining the best architectural model for predicting open unemployment in Indonesia. In IOP Conference Series: Materials Science and Engineering (Vol. 725, No. 1, p. 012115). IOP Publishing.
PCN. (2016). BMKG Kupang: NTT Sedang Musim Peralihan. https://www.beritasatu.com/nasional/364945/bmkg-kupang-ntt-sedang-musim-peralihan (accessed on 4/9/2022).
DOI: http://dx.doi.org/10.17737/tre.2022.8.2.00143
Refbacks
- There are currently no refbacks.
Copyright (c) 2022 Andri Gunawan, Suyono Thamrin, Yanif Dwi Kuntjoro, Abdi Manab Idris
This work is licensed under a Creative Commons Attribution 4.0 International License.
This work is licensed under a Creative Commons Attribution 4.0 License.Copyright @2014-2025 Trends in Renewable Energy (ISSN: 2376-2136, online ISSN: 2376-2144)