International Journal on Soft Computing ( IJSC ): January 2021

Friday, 29 January 2021

Interval Type-2 Fuzzy Neural Networks for Short Term Electric Load Forecasting: A Comparative Study

Uduak Umoh, Ini Umoeka, Mfon Ntekop and Emmanuel Babalola

Department of Computer Science, University of Uyo, Akwa Ibom State, 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

http://airccse.org/journal/ijsc/current2018.html

Tuesday, 19 January 2021

Analytical Formulations for the Level Based Weighted Average Value of Discrete Trapezoidal Fuzzy Numbers

Resmiye Nasiboglu1* , Rahila Abdullayeva2

1Department of Computer Science, Dokuz Eylul University, Izmir, Turkey

2Department of Informatics, Sumgait State University, Sumgait, Azerbaijan

ABSTRACT

In fuzzy decision-making processes based on linguistic information, operations on discrete fuzzy numbers are commonly performed. Aggregation and defuzzification operations are some of these often used operations. Many aggregation and defuzzification operators produce results independent to the decisionmaker’s strategy. On the other hand, the Weighted Average Based on Levels (WABL) approach can take into account the level weights and the decision maker's "optimism" strategy. This gives flexibility to the WABL operator and, through machine learning, can be trained in the direction of the decision maker's strategy, producing more satisfactory results for the decision maker. However, in order to determine the WABL value, it is necessary to calculate some integrals. In this study, the concept of WABL for discrete trapezoidal fuzzy numbers is investigated, and analytical formulas have been proven to facilitate the calculation of WABL value for these fuzzy numbers. Trapezoidal and their special form, triangular fuzzy numbers, are the most commonly used fuzzy number types in fuzzy modeling, so in this study, such numbers have been studied. Computational examples explaining the theoretical results have been performed.

KEYWORDS

Fuzzy number;Trapezoidal; Weighted level-based averaging; Defuzzification.

ORIGINAL SOURCE URL: https://aircconline.com/ijsc/V9N3/9318ijsc01.pdf

http://airccse.org/journal/ijsc/current2018.html