Published

2017-01-01

Long-term prediction of wind speed in La Serena City (Chile) using hybrid neural network-particle swarm algorithm

Predicción a largo plazo de la velocidad de viento en la ciudad de La Serena (Chile) utilizando un algoritmo híbrido de rojo neuronal-enjambre de partículas

DOI:

https://doi.org/10.15446/esrj.v21n1.50337

Keywords:

Wind speed, time series forecasting, artificial neural network, particle swarm optimization, meteorological data (en)
Velocidad del viento, predicción de series de tiempo, redes neuronales artificiales, optimización de enjambre de particulas (es)

Downloads

Authors

  • Juan A Lazzús Universidad de La Serena
  • Ignacio Salfate Universidad de La Serena

An artificial neural network was used for forecasting of long-term wind speed data (24 and 48 hours ahead) in La Serena City (Chile). In order to obtain a more effective correlation and prediction, a particle swarm algorithm was implemented to update the weights of the network. 43800 data points of wind speed were used (years 2003- 2007), and the past values of wind speed, relative humidity, and air temperature were used as input parameters, considering that these meteorogical parameters are more readily available around the globe. Several neural network architectures were studied, and the optimum architecture was determined by adding neurons in systematic form and evaluating the root mean square error (RMSE) during the learning process. The results show that the meteorological variables used as input parameters, have influential effects on the good training and predicting capabilities of the chosen network, and that the hybrid neural network can forecast the hourly wind speed with acceptable accuracy, such as: RMSE=0.81 [m·s−1], MSE=0.65 [m·s−1] 2 and R2 =0.97 for 24-hours-ahead wind speed prediction, and RMSE=0.78, MSE=0.634 [m·s−1] 2 and R2 =0.97 for 48-hours-ahead wind speed prediction.

Una red neuronal artificial fue utilizada para la predicción de datos de la velocidad de viento a largo plazo (24 y 48 horas en adelanto) en la Ciudad de La Serena (Chile). Para obtener una efectiva correlación y predición, se implementó una optimización de enjambre de particulas para actualizar los pesos de la red. Se emplearon 43800 datos de velocidad de viento (años 2003-2007), y los valores pasados de velocidad del viento, humedad relativa y temperatura del aire fueron utilizados como parámetros de entrada, considerando que estos parámetros meteorológicos se encuentran fácilmente disponibles en todo el mundo. Se estudiaron varias arquitecturas de redes neuronales y la arquitectura optima se determine añadiendo neuronas de forma sistemática y evaluando la raíz del error cuadrático medio (RMSE) durante el proceso de aprendizaje. Los resultados muestran que las variables meteorológicas utilizadas como parámetros de entrada, tienen un efecto positivo sobre el correcto entrenamiento y capacidades predictivas de la red, y que la red neural híbrida puede pronosticar la velocidad del viento horaria con una precisión aceptable, como un RMSE=0.81 [m·s−1], MSE=0.65 [m·s−1] 2 y R2 =0.97 para la predicción de la velocidad del viento de 24 horas en adelanto, y un RMSE=0.78, MSE=0.634 [m·s−1] 2 and R2 =0.97 para la predicción de la velocidad del viento de 48 horas en adelanto.

References

Akdağ, S.A. and Güler Ö. (2011). A Comparison of Wind Turbine Power Curve Models. Energy Sources Part A, 33, 2257–2263.

Awad, M., Pomares, H., Rojas, I., Salameh, O. and Hamdon, M. (2009). Prediction of time series using RBF neural networks: a new approach of clustering. International Arab Journal of Information Technology, 6, 138–143.

Bersini, H., Duchateau, A. and Bradshaw, N. (1997). Using incremental learning algorithms in the search for minimal effective fuzzy models. In: Proceedings of 6th International Conference on Fuzzy Systems, pp. 1417–1422.

Bezruchko, B.P., Karavaev, A.S., Ponomarenko, V.I. and Prokhorov, M.D. (2001). Reconstruction of time-delay systems from chaotic time series. Physical Review E, 64, 056216.

Çam, E. and Yildiz, O. (2006). Prediction of wind speed and power in the Central Anatolian region of Turkey by adaptative neuro-fuzzy inference systems (ANFIS). Turkish Journal of Engineering and Environmental Sciences, 30, 35–41.

Chng, E.S., Chen, S. and Mulgrew, B. (1996). Gradient radial basis function networks for nonlinear and nonstationary time series prediction. IEEE Transactions on Neural Networks, 7, 190–194.

Chua, L.O., Kocarev, L., Eckert, K. and Itoh, M. (1992). Experimental chaos synchronization in Chua’s circuit. International Journal of Bifurcation and Chaos, 2, 705–708.

Eberhart, R.C. and Kennedy, J. (1995). A new optimizer using particle swarm theory. In: Proceedings of 6th International Symposium on Micro Machine and Human Science, Nagoya. New York, NY, USA: IEEE, pp. 39–43.

Farmer, J.D. (1982). Chaotic attractors of an infinite-dimensional dynamical system. Physica D, 4, 366–393.

Freeman, J.A. and Skapura, D.M. (1991). Neural networks: algorithms, applications and programming techniques. Computation and Neural Systems Series. Massachusetts, USA: Addison-Wesley.

Hagan, M.T. and Menhaj, M.B. (1994). Training feedforward networks with the Marquardt algorithm, IEEE Transactions on Neural Networks, 5, 989–993.

Han, M. and Wang, Y. (2009). Analysis and modeling of multivariate chaotic time series based on neural network. Expert Systems with Applications, 36, 1280–1290.

Hanağasioğlu, M. (1999). Wind energy in Turkey. Renewable Energy, 16, 822–827.

Ikeda, K. (1979). Multiple-valued stationary state and its instability of the transmitted light by a ring cavity system. Optics Communications, 30, 257–261.

Kallos, G., Galanis, G. and Katsafados, P. (2007). Local wind speed forecasting and applications to power prediction. Geophysical Research, 9, 93–99.

Kalthoff, N., Bischoff-Gauß, I., Fiebig-Wittmaack, M., Fiedler, F., Thürauf, J., Novoa, E., Pizarro, C., Castillo, R., Gallardo, L., Rondanelli, R. and Kohler, M. (2002). Mesoscale wind regimes in Chile at 30º S. Journal of Applied Meteorology, 41, 953–970.

Kalthoff, N., Fiebig-Wittmaack, M., Meißner, C., Kohler, M., Uriarte, M. and Bischoff-Gauß, I. (2006). The energy balance, evapo-transpiration and nocturnal dew deposition of an arid valley in the Andes. Journal of Arid Environments, 65, 420–443.

Karunasinghe, D.S.K. and Liong, S.Y. (2006). Chaotic time series prediction with a global model: artificial neural network. Journal of Hydrology, 323, 92–105.

Kennedy, J., Eberhart, R.C. and Shi, Y. (2001). Swarm Intelligence. San Diego, CA, USA: Academic Press.

Kulkarni, M.A., Patil, S., Rama, G.V. and Sen, P.N. (2008). Wind speed prediction using statistical regression and neural nerwork. Journal of Earth System Science, 117, 457–463.

Lazzús, J.A. (2011). Predicting natural and chaotic time series with a swarm-optimized neural network. Chinese Physics Letters, 28, 110504.

Lazzús, J.A. (2013). Neural network-particle swarm modeling to predict thermal properties. Mathematical and Computer Modelling, 57, 2408–2418.

Lazzús, J.A., Salfate, I. and Montecinos, S. (2014). Hybrid neural network–particle swarm algorithm to describe chaotic time series. Neural Network World, 24, 601–617.

Liu, H., Tian, H., Pan, D. and Li, Y. (2013). Forecasting models for wind speed using wavelet, wavelet packet, time series and artificial neural networks. Applied Energy, 107, 191–208.

Mackey, M.C. and Glass, L. (1977). Oscillation and chaos in physiological control systems. Science, 197, 287–289.

Martinetz, T.M., Berkovich, S.G. and Schulten, K.J. (1993). Neural-gas network for vector quantization and its application to time-series prediction. IEEE Transactions on Neural Networks, 4, 558–569.

Meinen, C.S. and McPhaden, M.J. (2000). Observations of warm water volume changes in the Equatorial Pacific and their relationship to El Niño and La Niña. Journal of Climate, 13, 3551–3559.

Mirzaee, H. (2009). Linear combination rule in genetic algorithm for optimization of finite impulse response neural network to predict natural chaotic time series. Chaos, Solitons & Fractals, 41, 2681–2689.

Monfared, M., Rastegar, H. and Madadi-Kojabadi, H. (2009). A new strategy for wind speed forecasting using artificial intelligent methods. Renewable Energy, 34, 845–848.

Pérez Ponce, A.A., Lazzús, J.A. and Palma-Chilla, L. Hybrid neural network–particle swarm method to predict global radiation over the Norte Chico (Chile). Journal of Renewable and Sustainable Energy, 4, 023108.

Velo, R., López, P. and Maseda, F. (2014). Wind speed estimation using multilayer perceptron. Energy Conversion and Management, 81, 1–9.

Wang, J., Zhang, W., Wang, J., Han, T. and Kong, L. A novel hybrid approach for wind speed prediction. Information Sciences, 273, 304–318.

Whitehead, B.A. and Choate, T.D. (1996). Cooperative-competitive genetic evolution of radial basis function centers and widths for time series prediction. IEEE Transactions on Neural Networks, 7, 869–880.

Zhang, W., Wu, J., Wang, J., Zhao, W. and Shen, J. (2012). Performance analysis of four modified approaches for wind speed forecasting. Applied Energy, 99, 324–333.

How to Cite

APA

Lazzús, J. A. and Salfate, I. (2017). Long-term prediction of wind speed in La Serena City (Chile) using hybrid neural network-particle swarm algorithm. Earth Sciences Research Journal, 21(1), 29–35. https://doi.org/10.15446/esrj.v21n1.50337

ACM

[1]
Lazzús, J.A. and Salfate, I. 2017. Long-term prediction of wind speed in La Serena City (Chile) using hybrid neural network-particle swarm algorithm. Earth Sciences Research Journal. 21, 1 (Jan. 2017), 29–35. DOI:https://doi.org/10.15446/esrj.v21n1.50337.

ACS

(1)
Lazzús, J. A.; Salfate, I. Long-term prediction of wind speed in La Serena City (Chile) using hybrid neural network-particle swarm algorithm. Earth sci. res. j. 2017, 21, 29-35.

ABNT

LAZZÚS, J. A.; SALFATE, I. Long-term prediction of wind speed in La Serena City (Chile) using hybrid neural network-particle swarm algorithm. Earth Sciences Research Journal, [S. l.], v. 21, n. 1, p. 29–35, 2017. DOI: 10.15446/esrj.v21n1.50337. Disponível em: https://revistas.unal.edu.co/index.php/esrj/article/view/50337. Acesso em: 28 mar. 2024.

Chicago

Lazzús, Juan A, and Ignacio Salfate. 2017. “Long-term prediction of wind speed in La Serena City (Chile) using hybrid neural network-particle swarm algorithm”. Earth Sciences Research Journal 21 (1):29-35. https://doi.org/10.15446/esrj.v21n1.50337.

Harvard

Lazzús, J. A. and Salfate, I. (2017) “Long-term prediction of wind speed in La Serena City (Chile) using hybrid neural network-particle swarm algorithm”, Earth Sciences Research Journal, 21(1), pp. 29–35. doi: 10.15446/esrj.v21n1.50337.

IEEE

[1]
J. A. Lazzús and I. Salfate, “Long-term prediction of wind speed in La Serena City (Chile) using hybrid neural network-particle swarm algorithm”, Earth sci. res. j., vol. 21, no. 1, pp. 29–35, Jan. 2017.

MLA

Lazzús, J. A., and I. Salfate. “Long-term prediction of wind speed in La Serena City (Chile) using hybrid neural network-particle swarm algorithm”. Earth Sciences Research Journal, vol. 21, no. 1, Jan. 2017, pp. 29-35, doi:10.15446/esrj.v21n1.50337.

Turabian

Lazzús, Juan A, and Ignacio Salfate. “Long-term prediction of wind speed in La Serena City (Chile) using hybrid neural network-particle swarm algorithm”. Earth Sciences Research Journal 21, no. 1 (January 1, 2017): 29–35. Accessed March 28, 2024. https://revistas.unal.edu.co/index.php/esrj/article/view/50337.

Vancouver

1.
Lazzús JA, Salfate I. Long-term prediction of wind speed in La Serena City (Chile) using hybrid neural network-particle swarm algorithm. Earth sci. res. j. [Internet]. 2017 Jan. 1 [cited 2024 Mar. 28];21(1):29-35. Available from: https://revistas.unal.edu.co/index.php/esrj/article/view/50337

Download Citation

CrossRef Cited-by

CrossRef citations2

1. Yongjie Li, Ka In Hoi, Kai Meng Mok, Ka Veng Yuen. (2023). Air Quality Monitoring and Advanced Bayesian Modeling. , p.245. https://doi.org/10.1016/B978-0-323-90266-3.00003-0.

2. Juan A. Lazzús, Pedro Vega-Jorquera, Ignacio Salfate, Fernando Cuturrufo, Luis Palma-Chilla. (2020). Variability and forecasting of air temperature in Elqui Valley (Chile). Earth Science Informatics, 13(4), p.1411. https://doi.org/10.1007/s12145-020-00519-9.

Dimensions

PlumX

Article abstract page views

650

Downloads

Download data is not yet available.