single-jc.php

JACIII Vol.22 No.6 pp. 900-906
doi: 10.20965/jaciii.2018.p0900
(2018)

Paper:

Simultaneous Forecasting of Meteorological Data Based on a Self-Organizing Incremental Neural Network

Wonjik Kim* and Osamu Hasegawa**,***

*Department of Systems and Control Engineering, Tokyo Institute of Technology
2-12-1-S5-22 Ookayama, Meguro, Tokyo 152-8550, Japan

**Department of Systems and Control Engineering, Tokyo Institute of Technology
4259 J3-13 Nagatsuta-cho, Midori-ku, Yokohama, Kanagawa 226-8501, Japan

***SOINN Inc.
Cureindo-building 405, 8-4-30 Turuma, Machida, Tokyo 194-0004, Japan

Received:
March 27, 2018
Accepted:
July 9, 2018
Published:
October 20, 2018
Keywords:
self-organizing incremental neural network, topology, forecasting model, meteorology, neural network
Abstract

In this study, we propose a simultaneous forecasting model for meteorological time-series data based on a self-organizing incremental neural network (SOINN). Meteorological parameters (i.e., temperature, wet bulb temperature, humidity, wind speed, atmospheric pressure, and total solar radiation on a horizontal surface) are considered as input data for the prediction of meteorological time-series information. Based on a SOINN within normalized-refined-meteorological data, proposed model succeeded forecasting temperature, humidity, wind speed and atmospheric pressure simultaneously. In addition, proposed model does not take more than 2 s in training half-year period and 15 s in testing half-year period. This paper also elucidates the SOINN and the algorithm of the learning process. The effectiveness of our model is established by comparison of our results with experimental results and with results obtained by another model. Three advantages of our model are also described. The obtained information can be effective in applications based on neural networks, and the proposed model for handling meteorological phenomena may be helpful for other studies worldwide including energy management system.

Cite this article as:
W. Kim and O. Hasegawa, “Simultaneous Forecasting of Meteorological Data Based on a Self-Organizing Incremental Neural Network,” J. Adv. Comput. Intell. Intell. Inform., Vol.22, No.6, pp. 900-906, 2018.
Data files:
References
  1. [1] T. P. Barnett and R. W. Preisendorfer, “Multifield analog prediction of short-term climate fluctuations using a climate state vector,” J. of the Atmospheric Sciences, Vol.35, Issue 10, pp. 1771-1787, 1978.
  2. [2] E. J. Becker, H. Van Den Dool, and M. Peña, “Short-term climate extremes: prediction skill and predictability,” J. of Climate, Vol.26, Issue 2, pp. 512-531, 2013.
  3. [3] H. M. Kim, P. J. Webster, and J. A. Curry, “Evaluation of short-term climate change prediction in multi-model CMIP5 decadal hindcasts,” Geophysical Research Letters, Vol.39, Issue 10, 2012.
  4. [4] C. Fu, Z. Jiang, Z. Guan, J. He, and Z. Xu, “Impacts of Climate Change on Water Resources and Agriculture in China,” Regional Climate Studies of China, pp. 447-464, 2008.
  5. [5] D. L. Swain, M. Tsiang, M. Haugen, D. Singh, A. Charland et al., “The extraordinary California drought of 2013/2014: Character, context, and the role of climate change,” Bulletin of the American Meteorological Society, Vol.95, No.9, p. S3, 2014.
  6. [6] Intergovernmental Panel on Climate Change, “Climate Change 2014-Impacts, Adaptation and Vulnerability: Regional Aspects,” Cambridge University Press, 2014.
  7. [7] D. B. Lobell, M. B. Burke et al., “Prioritizing climate change adaption needs for food security in 2030,” Science, Vol.319, Issue 5863, pp. 607-610, 2008.
  8. [8] S. S. Soman et al., “A review of wind power and wind speed forecasting methods with different time horizons,” North American Power Symposium, 2010.
  9. [9] B. Doucoure, K. Agbossou, and A. Cardenas, “Time series prediction using artificial wavelet neural network and multi-resolution analysis: Application to wind speed data,” Renewable Energy, Vol.92, pp. 202-211, 2016.
  10. [10] L. Ma et al., “A review on the forecasting of wind speed and generated power,” Renewable and Sustainable Energy Reviews, Vol.13, Issue 4, pp. 915-920, 2009.
  11. [11] S. Marras, J. F. Kelly et al., “A review of element-based Galerkin methods for numerical weather prediction: Finite elements, spectral elements, and discontinuous Galerkin,” Archives of Computational Methods in Engineering, Vol.23, Issue 4, pp. 673-722, 2016.
  12. [12] I. Sandu, A. Beljaars, P. Bechtold, T. Mauritsen, and G. Balsamo, “Why is it so difficult to represent stably stratified conditions in numerical weather prediction (NWP) models?,” J. of Advances in Modeling Earth Systems, Vol.5, Issue 2, pp. 117-133, 2013.
  13. [13] E. Erdem and J. Shi, “ARMA based approaches for forecasting the tuple of wind speed and direction,” Applied Energy, Vol.88, Issue 4, pp. 1405-1414, 2011.
  14. [14] J. Wang and J. Hu., “A robust combination approach for short-term wind speed forecasting and analysis–Combination of the ARIMA (Autoregressive Integrated Moving Average), ELM (Extreme Learning Machine), SVM (Support Vector Machine) and LSSVM (Least Square SVM) forecasts using a GPR (Gaussian Process Regression) model,” Energy, Vol.93, Part 1 pp. 41-56, 2015.
  15. [15] J. P. S. Catalao, H. M. I. Pousinho, and V. M. F. Mendes, “Hybrid wavelet-PSO-ANFIS approach for short-term wind power forecasting in Portugal,” IEEE Trans. on Sustainable Energy, Vol.2, Issue 1, pp. 50-59, 2011.
  16. [16] Y. Liu et al., “A hybrid forecasting method for wind power ramp based on Orthogonal Test and Support Vector Machine (OT-SVM),” IEEE Trans. on Sustainable Energy, Vol.8, Issue 2, pp. 451-457, 2017.
  17. [17] J. Wang et al., “Hybrid forecasting model-based data mining and genetic algorithm-adaptive particle swarm optimisation: a case study of wind speed time series,” IET Renewable Power Generation, Vol.10, Issue 3, pp. 287-298, 2016.
  18. [18] F. Shen and O. Hasegawa, “An incremental network for on-line unsupervised classification and topology learning,” Neural Networks, Vol.19, Issue 1, pp. 90-106, 2006.
  19. [19] J. M. Zurada, “Introduction to artificial neural systems,” Vol.8, West Publishing, 1992.
  20. [20] K. Yamasaki et al., “Self-Organizing Incremental Neural Network-SOINN-and Its Usage,” Japan Neural Network Society, Vol.17, Issue 4, pp. 187-196, 2010.
  21. [21] W. Kim and O. Hasegawa, “Time Series Prediction of Tropical Storm Trajectory Using Self-Organizing Incremental Neural Networks and Error Evaluation,” J. Adv. Comput. Intell. Intell. Inform., Vol.22, No.4, pp. 465-474, 2018.
  22. [22] W. Kim and O. Hasegawa, “Prediction of Tropical Storms Using Self-organizing Incremental Neural Networks and Error Evaluation,” Int. Conf. on Neural Information Processing, pp. 846-855, Springer, Cham, 2017.
  23. [23] F. Shen, O. Tomotaka, and O. Hasegawa, “An enhanced self-organizing incremental neural network for online unsupervised learning,” Neural Networks, Vol.20, Issue 8, pp. 893-903, 2007.
  24. [24] T. Kohonen, “The self-organizing map,” Proc. of the IEEE, Vol.78, Issue 9, pp. 1464-1480, 1990.
  25. [25] B. Fritzke, “A growing neural gas network learns topologies,” Advances in Neural Information Processing Systems, 1995.
  26. [26] M. Dittenbach, D. Merkl, and A. Rauber, “The growing hierarchical self-organizing map,” Proc. of the IEEE-INNS-ENNS Int. Joint Conf. on Neural Networks (IJCNN 2000), Vol.6, 2000.
  27. [27] Y. Nakamura and O. Hasegawa, “Nonparametric Density Estimation Based on Self-Organizing Incremental Neural Network for Large Noisy Data,” IEEE Trans. on Neural Networks and Learning Systems, Vol.28, Issue 1, pp. 8-17, 2016.
  28. [28] K. Kumar and V. K. Jain, “Autoregressive integrated moving averages (ARIMA) modelling of a traffic noise time series,” Applied Acoustics, Vol.58, Issue 3, pp. 283-294, 1999.
  29. [29] V. S. Ediger and S. Akar, “ARIMA forecasting of primary energy demand by fuel in Turkey,” Energy Policy, Vol.35, Issue 3, pp. 1701-1708, 2007.
  30. [30] S. Bhandari, N. Bergmann et al., “Time Series Data Analysis of Wireless Sensor Network Measurements of Temperature,” Sensors, Vol.17, Issue 6, pp. 1221, 2017.
  31. [31] G. E. Box and D. A. Pierce, “Distribution of residual autoregressive-integrated moving average time series models,” J. of the American Statistical Association, Vol.65, Issue 332, pp. 1509-1526, 1970.
  32. [32] D. Ren, H. Li, and Y. Ji., “Home energy management system for the residential load control based on the price prediction,” Online Conf. on Green Communications, pp. 1-6, 2011.
  33. [33] S. J. Kang, J. Park, K.-Y. Oh, J. G. Noh, and H. Park, “Scheduling-based real time energy flow control strategy for building energy management system,” Energy and Buildings, Vol.75, pp. 239-248, 2014.
  34. [34] K. Tanaka, H. Watanabe, and A. Endou, “Enerize E3 Factory Energy Management System,” Yokogawa Technical Report English Edition, Vol.53, No.1, pp. 24-26, 2010.
  35. [35] L. Willis and P. M. Jignesh, “Energy management for MapReduce clusters,” Proc. of the VLDB Endowment, Vol.3, Issue 1-2, pp. 129-139, 2010.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, IE9,10,11, Opera.

Last updated on Nov. 16, 2018