CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
Reseach Article

Design of a Fuzzy Time Series Forecasting Model for Hydro Power Generation

by Poornima Devi, C Vijaya Lakshmi, E. Sakthivel
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 74 - Number 16
Year of Publication: 2013
Authors: Poornima Devi, C Vijaya Lakshmi, E. Sakthivel
10.5120/12966-7719

Poornima Devi, C Vijaya Lakshmi, E. Sakthivel . Design of a Fuzzy Time Series Forecasting Model for Hydro Power Generation. International Journal of Computer Applications. 74, 16 ( July 2013), 1-5. DOI=10.5120/12966-7719

@article{ 10.5120/12966-7719,
author = { Poornima Devi, C Vijaya Lakshmi, E. Sakthivel },
title = { Design of a Fuzzy Time Series Forecasting Model for Hydro Power Generation },
journal = { International Journal of Computer Applications },
issue_date = { July 2013 },
volume = { 74 },
number = { 16 },
month = { July },
year = { 2013 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume74/number16/12966-7719/ },
doi = { 10.5120/12966-7719 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:42:26.088881+05:30
%A Poornima Devi
%A C Vijaya Lakshmi
%A E. Sakthivel
%T Design of a Fuzzy Time Series Forecasting Model for Hydro Power Generation
%J International Journal of Computer Applications
%@ 0975-8887
%V 74
%N 16
%P 1-5
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper mainly deals with the design of forecasting model for Hydro power generation using Fuzzy time series. The fuzzy time series has recently received an increasing attention because of its capability of dealing with vague and incomplete data. There have been a variety of models developed either to improve forecasting accuracy or reduce computation overhead. This technique has been applied to forecast various fields and have been shown to forecast better than other models. Hence, in this paper fuzzy time series forecasting technique has been applied on hydro power generation data set. An algorithm is designed and based on the numerical calculations and graphical representations it reveals that Hydro Power generation can be forecasted by using Fuzzy Time Series.

References
  1. Aladag Cagdas H. , Basaran Murat A. , Egrioglu Erol, Yolcu Ufuk and Uslu Vedide R. (2009): Forecasting in high order fuzzy times series by using neural networks to define fuzzy relations, Expert Systems with Applications, Vol. 36, pp. 4228–4231.
  2. Aznarte José Luis, Alcalá-Fdez Jesús, Arauzo-Azofra Antonio and Benítez José Manuel (2012): Financial time series forecasting with a bio-inspired fuzzy model, Expert Systems with Applications, Vol. 39, pp. 12302–12309.
  3. Bajestani Narges Shafaei and Zare Assef (2011): Forecasting TAIEX using improved type 2 fuzzy time series, Expert Systems with Applications, Vol. 38, pp. 5816–5821.
  4. Cheng Ching-Hsue, Wang Jia-Wen and Li Chen-Hsun (2008): Forecasting the number of outpatient visits using a new fuzzy time series based on weighted-transitional matrix, Expert Systems with Applications Vol. 34, pp. 2568–2575.
  5. Cheng Ching-Hsue, Chen Tai-Liang, Teoh Hia Jong and Chiang Chen-Han (2008): Fuzzy time-series based on adaptive expectation model for TAIEX forecasting, Expert Systems with Applications, Vol. 34, pp. 1126–1132.
  6. Chi-Chen Wang, (2011): A comparison study between fuzzy time series model and ARIMA model for forecasting Taiwan export, Expert Systems with Applications Vol. 38, pp. 9296–9304.
  7. Chunshien Li and Jhao-WunHu (2012): A new ARIMA-based neuro-fuzzy approach and swarm intelligence for time series forecasting, Engineering Applications of Artificial Intelligence, Vol. 25, pp. 295–308.
  8. Duru Oken, Bulut Emrah and Yoshida Shigeru (2010): Bivariate Long Term Fuzzy Time Series Forecasting of Dry Cargo Freight Rates, The Asian Journal of shipping and Logistics, Vol. 28, No. 2, pp. 205-223.
  9. Egrioglu Erol, Aladag Cagdas Hakan, Yolcu Ufuk, Basaran Murat A. and Uslu Vedide R. (2009): A new hybrid approach based on SARIMA and partial high order bivariate fuzzy time series forecasting model, Expert Systems with Applications Vol. 36, pp. 7424–7434.
  10. Egrioglu Erol, Aladag Cagdas Hakan and Yolcu Ufuk (2013): Fuzzy time series forecasting with a novel hybrid approach combining fuzzy c-means and neural networks, Expert Systems with Applications, Vol. 40, pp. 854–857.
  11. Enjian Bai, W. K. Wong, W. C. Chu, Min Xia, and Feng Pan, (2011): A heuristic time-invariant model for fuzzy time series forecasting, Expert Systems with Applications vol. 38, pp. 2701–2707.
  12. Kunhuang Huarng and Hui-Kuang Yu (2005): A Type 2 fuzzy time series model for stock index forecasting, Physica A, Vol. 353, pp. 445–462.
  13. Lia Sheng-Tun, Cheng Yi-Chung (2007): Deterministic fuzzy time series model for forecasting enrollments, Computers and Mathematics with Applications, Vol. 53, pp. 1904–1920.
  14. Liu (2009): An integrated fuzzy time series forecasting system, Expert Systems with Applications, Vol. 36, pp. 10045–10053.
  15. Liu Hao-Tien and Wei Mao-Len (2010): An Improved Fuzzy Forecasting Method for Seasonal Time Series, Expert Systems with Applications, Vol. 37, pp. 6310–6318.
  16. Pierpaolo D'Urso and Elizabeth Ann Maharaj (2009): Autocorrelation-based fuzzy clustering of time series, Fuzzy Sets and Systems, Vol. 160, pp. 3565–3589
  17. Qiang Song (2003): A Note on Fuzzy Time Series Model Selection with Sample Autocorrelation Functions, Cybernetics and Systems: An International Journal, Vol. 34, pp. 93-107.
  18. RafiulHassan Md. , Nath Baikunth, Kirley Michael, Kamruzzaman Joarder (2011): A hybrid of multi objective Evolutionary Algorithm and HMM-Fuzzy model for time series prediction, Neuro computing Vol. 81, pp. 1–11.
  19. Reuter. U. , and Moller, B. , (2010): Artificial Neural Networks for Forecasting of Fuzzy Time Series, Computer-Aided Civil and Infrastructure Engineering, Vol. 25, pp. 363–374.
  20. Ruey-Chyn Tsaur,(2012): A Fuzzy Time Series-Markov Chain Model with an Application to Forecast the Exchange Rate Between the Taiwan and US Dollar, International Journal of Innovative Computing, Information and Control,Vol. 8, No. 7(B), pp. 4931- 4942.
  21. Shah Mrinalini (2012): Fuzzy based trend mapping and forecasting for time series data, Expert Systems with Applications, Vol. 39, pp. 6351–6358.
  22. Sheng-Tun Li, Shu-Ching Kuo, Yi-Chung Cheng and Chih-ChuanChen (2010): Deterministic vector long-term forecasting for fuzzy time series, Fuzzy Sets and Systems, Vol. 161, pp. 1852–1870.
  23. Shiva Raj Singh,(2008): A computational method of forecasting based on fuzzy time series, Mathematics and Computers in Simulation Vol. 79, pp. 539–554.
  24. Shiva Raj Singh, (2009): A computational method of forecasting based on high-order fuzzy time series, Expert Systems with Applications Vol. 36, pp. 10551–10559.
  25. Shiva Raj Singh (2007): A Robust Method of Forecasting based on Fuzzy Time Series, Applied Mathematics and Computation, Vol. 188, pp. 472–484.
  26. Tiffany Hui-Kuang Yu and Kun-Huang Huarng (2008): A bivariate fuzzy time series model to forecast the TAIEX, Expert Systems with Applications Vol. 34, pp. 2945–2952.
  27. Wangren Qiu, Xiaodong Liu and Hailin Li (2011): A generalized method for forecasting based on fuzzy time series, Expert Systems with Applications Vol. 38, pp. 10446–10453.
  28. Wong Hsien-Lun, Tu Yi-Hsien and Wang Chi-Chen (2010): Application of fuzzy time series models for forecasting the amount of Taiwan export, Expert Systems with Applications, Vol. 37, pp. 1465–1470.
Index Terms

Computer Science
Information Sciences

Keywords

Fuzzy forecasting HydroPower generation Fuzzy time series uncertainity