Electrical Load Forecasting using GFF Neural Network-A Sensitivity Analysis Perspective

Pavithra Khajipeta  and S  Hemachandra

doi:10.17485/ijst/2017/v10i25/112807

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Electrical Load Forecasting using GFF Neural Network-A Sensitivity Analysis Perspective

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Indian Journal of Science and Technology

DOI: 10.17485/ijst/2017/v10i25/112807

Year: 2017, Volume: 10, Issue: 25, Pages: 1-5

Original Article

Electrical Load Forecasting using GFF Neural Network-A Sensitivity Analysis Perspective

Pavithra Khajipeta^* and S. Hemachandra

Department of Electrical Engineering, Sree Vidyanikethan Engineering College,Tirupathi – 517102, Andhra Pradesh, India; [email protected], [email protected]

^*Author for correspondence
Pavithra Khajipeta
Department of Electrical Engineering, Sree Vidyanikethan Engineering College,Tirupathi – 517102, Andhra Pradesh, India; [email protected]

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objective: Prediction of Short term electrical load forecasting with the help of Sensitivity Analysis. Methods/Statistical Analysis: Traditional and Intelligent methods are available for electrical load forecasting. As the results from traditional methods are not accurate, modern methods like neural networks are preferred to predict the electrical load. Findings: There are various parameters used for prediction of electrical load. By performing sensitivity analysis significant inputs can be identified and load can be predicted. Accuracy is maintained even after performing sensitivity analysis. Application/ Improvements: The complexity of the electrical load forecasting system can be reduced.

Keywords: Forecast, Neural network (NN), Short-Term Load Forecasting (STLF), Sensitivity Analysis