Earticle

초록

영어

Load forecasting plays a major role in planning and operation of a power system. Many techniques are available in the literature among these neural networks, linear multiple regression, regression trees, curve fitting and averaging models are the most popular because these models gives accurate solutions with very less tolerable Least Mean Absolute Percent Error(MAPE). In this paper a comparative study was made between these forecasting models and it was found that when compared to the four independent models, the averaging model i.e. combination of Curve Fitting, Regression Trees & Neural Network gives less MAPE. MATLAB programming results validates that averaging model gives better performance than individual models.

목차

Abstract
 1. Introduction
  1.1. Techniques for Load Forecasting
 2. Procedure
 3. Discussion
 4. Results
 5. Comparison
 6. Conclusion
 Acknowledgments
 References

키워드

저자

• Kumar Reddy Cheepati [ Asst. Professor, Dept. of E.E.E, S.V.E.C, Tirupathi, A.P, India ]
• T. Nageswara Prasad [ Professor & Head, Dept. of E.E.E, S.V.E.C, Tirupathi, A.P, India ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Grid and Distributed Computing

• 간기

  격월간

• pISSN

  2005-4262

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605