Data Selection and Fuzzy-Rules Generation for ShortTerm Load Forecasting Using ANFIS
Abstract: This paper focused
on data analysis, with aim of determining the actual variables that affect the load
consumption in short term electric load forecasting. Correlation analysis was
used to determine how the load consumption is related to the forecasting
variables (model inputs), and hypothesis test was used to justify the
correlation coefficient of each variable. Three different models based on data
selection criteria where tested using Adaptive Neuro-Fuzzy Inference System
(ANFIS). Subtractive Clustering (SC) and Fuzzy c-means (FCM) rules generation
algorithms ware compared in all the three models. It was observed that
forecasting using Hypothesis test data with SC algorithm gave better accuracy
compared to the other two approaches. But FCM algorithm is faster in all the
three approaches. In conclusion, hypothesis test on the correlation coefficient
of the data is a commendable practice for data selection and analysis in
shortterm load forecasting.
Keywords: short-term load
forecasting, anfis, clustering algorithm, correlation analysis, hypothesis test
Author: M. Mustapha, M. W.
Mustafa, S. N. Khalid
Journal Code: jptkomputergg160273
