Uduak Umoh, Ini Umoeka, Mfon Ntekop and Emmanuel Babalola, University of Uyo, Nigeria
ABSTRACT
This paper focuses on the study of short term load forecasting (STELF) using interval Type-2 Fuzzy Logic (IT2FL) and feed-forward Neural Network with back-propagation (NN-BP) tuning algorithm to improve their approximation capability, flexibility and adaptiveness. IT2FL for STELF is presented which provides additional degrees of freedom for handling more uncertainties for improving prediction accuracy and reducing cost. The IT2FL comprises five components which include; the fuzzification unit, the knowledge base, the inference engine, the type reducer and the defuzzification unit. Gaussian membership function is used to show the degree of membership of the input variables. The lower and upper membership functions (fired rules) as well as their consequent coefficients of IT2FL are fed into a (NN) which produces a crisp value coresponding to the optimal defuzzified output of IT2FLSs. The NN type reducer is trained to optimize parameters of membership function (MF) so as to produce an output with minimum error function with the purpose of improving forecasting performance of IT2FLS models. The IT2FNN system has the ability to overcome the limitations of individual technique and enhances their strengths to handle electric load forecasting. The IT2FNN is applied for STELF in Akwa Ibom State-Nigeria. The result of performance of IT2FNN is compared with IT2FLS and T1FLS methods for short term load forecasting with MSE of 0.00123, 0.00185 and 0.00247 respectively. Also, the results of forecasting are compared using RMSE of 0.035, 0.043 and 0.035 respectively, indicating a best accurate forecasting with IT2FNN. In addition, the result of performance of IT2FNN is compared with IT2FLS and T1FLS methods for short term load forecasting with MAPE of 1.5%, 3% and 4.5% respectively. Simulation results show that the IT2FNN approach takes advantages of accuracy and efficiency and performs better in prediction than IT2FL and T1FL methods in power load forecasting task.
KEYWORDS
Interval type-2 fuzzy logic; feed-forward neural network; back propagation; electric load; Interval type-2 fuzzy neural networks; type-1 fuzzy logic.
Original Source URL: https://aircconline.com/ijsc/V9N1/9118ijsc01.pdf
https://airccse.org/journal/ijsc/current2018.html
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