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논문검색

Performance Comparison of Short Term Load Forecasting Techniques

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

저자정보

  • 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

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