• 대한전기학회
Mobile QR Code QR CODE : The Transactions of the Korean Institute of Electrical Engineers
  • COPE
  • kcse
  • 한국과학기술단체총연합회
  • 한국학술지인용색인
  • Scopus
  • crossref
  • orcid
The Transactions of the Korean Institute of Electrical Engineers

The Transactions of the Korean Institute of Electrical Engineers

ISO Journal TitleTrans. Korean. Inst. Elect. Eng.
SCImago Journal & Country Rank

Journal Search

  1. Home
  2. Archive
  3. 2019-04 (Vol.68 No.04)
  4. 10.5370/KIEE.2019.68.4.558
XML PDF INFO REF

References

1 
C. M. Pereira, N. N. Almeida, M. L. F. Velloso, 2015, Fuzzy modeling to forecast an electric load time series, Procedia Computer Science, Vol. 55, pp. 395-404DOI
2 
H. H. Steinherz, C. E. Pedreira, R. C. Souza, 2001, Neural networks for short-term load forecasting: A review and evaluation, IEEE Trans. on Power Systems, Vol. 16, No. 1, pp. 44-55DOI
3 
B. H. Ahn, H. R. Choi, H. C. Lee, 2015, Regional Long- Term/Mid-Term Load Forecasting using SARIMA in South Korea, Journal of the Korea Academia- Industrial Cooperation Society, Vol. 16, No. 12, pp. 8576-8584DOI
4 
H. W. Jung, K. B. Song, 2014, Daily Maximum Electric Load Forecasting for the Next 4 Weeks for Power System Maintenance and Operation, The Transactions of the Korean Institute of Electrical Engineers, Vol. 63, pp. 1497-1502DOI
5 
J. H. Puiar, 2010, Fuzzy Ideology based Long Term Load Forecasting, World Academy of Science, Engineering and Technology, Vol. 40, pp. 640-645Google Search
6 
D. Ali, M. Yohanna, M. I. Puwu, B. M. Garkida, 2016, Long- term load forecast modelling using a fuzzy logic approach, Pacific Science Review A: Natural Science and Engineering, Vol. 18, pp. 123-127DOI
7 
C. C. Hsu, C. Y. Chen, 2003, Regional load forecasting in Taiwan-applications of artificial neural networks, Energy Conversion and Management, Vol. 44, pp. 1941-1949DOI
8 
M. Hayati, Y. Shirvany, 2007, Artificial Neural Network Approach for Short Term Load Forecasting for Illam Region, IJECSE, Vol. 1, No. 2, pp. 121-125Google Search
9 
P. F. Pai, W. C. Hong, 2005, Support vector machines with simulated annealing algorithms in electricity load forecasting, Energy Conversion and Management, Vol. 46, pp. 2669-2688DOI
10 
J. Dhillon, S. A. Rahman, S. U. Ahmad, M. J. Hossain, 2016, Peak Electricity Load Forecasting Using Online Support Vector Regression, 2016 IEEE Canadian Conference on Electrical and Computer Engineering, pp. 1-5DOI
11 
Y. K. Bang, J. H. Kim, C. H. Lee, 2017, Daily Peak Electric Load Forecasting Using Neural Network and Fuzzy System, The Transactions of the Korean Institute of Electrical Engineers, Vol. 67, No. 1, pp. 96-102Google Search
12 
J. M. Mendal, 2001, Uncertain Rule-based Fuzzy Logic System: Introduction and New Directions, Prentice- Hall, Upper Saddle River, Nj 07458Google Search
13 
Y. K. Bang, C. H. Lee, 2011, Fuzzy Time Series Prediction using Hierarchical Clustering Algorithms, Expert Systems with Applications, Vol. 38, pp. 4312-4325DOI
14 
https://home.kepco.co.kr/kepco/main.doGoogle Search