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All Journal International Journal of Power Electronics and Drive Systems (IJPEDS)
Mohammad Shahid
Galgotias College of Engineering and Technology
Control & Systems Engineering Electrical & Electronics Engineering

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Comparison of fuzzy time series, ANN and wavelet techniques for short term load forecasting Shahida Khatoon; Ibraheem Ibraheem; Priti Gupta; Mohammad Shahid
International Journal of Power Electronics and Drive Systems (IJPEDS) Vol 14, No 2: June 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijpeds.v14.i2.pp1260-1269

Abstract

The present article presents the load forecasting for a power system (substation) load demands using techniques based on fuzzy time series (FTS), artificial neural network (ANN), and wavelet transform (WT). The mean absolute percentage error (MAPE), integral absolute error (IAE), integral of time multiplied error (ITAE), integral square error (ISE) along with integral time multiplied square error (ITSE) criteria are used for determining the performance indices and minimizing the error. From the investigations of the results obtained in the study, it is inferred that forecasting of electric load based on WT and ANN offers less error as compared to FTS. The suggested integrated model captures the useful properties of artificial neural networks and wavelet transforms in time series and is found to be accurate for real-time data.
Co-Authors Ibraheem Ibraheem Priti Gupta Shahida Khatoon