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Electrical Load Forecasting using GFF Neural Network-A Sensitivity Analysis Perspective
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.
Forecast, Neural network (NN), Short-Term Load Forecasting (STLF), Sensitivity Analysis.
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