ELECTRICA
Original Article

Performance Comparison of Hybrid Neuro-Fuzzy Models using Meta-Heuristic Algorithms for Short-Term Wind Speed Forecasting

1.

Department of Electrical and Electronics Engineering, Energy Technology Application and Research Center, Bilecik Şeyh Edebali University, Bilecik, Turkey

2.

Department of Computer Engineering, Bilecik Şeyh Edebali University, Bilecik, Turkey

ELECTRICA 2021; 21: 305-321
DOI: 10.5152/electrica.2021.21042
Read: 66 Downloads: 38 Published: 23 August 2021

In this paper, short-term wind speed forecasting models have been developed using neuro-fuzzy systems. The optimal neuro-fuzzy model has been investigated in detail. In addition, meta-heuristic algorithms, such as artificial bee colony differential evolution genetic algorithm and particle swarm optimization for training adaptive neuro-fuzzy inference systems parameters have been used in this study. This is a novel study, as four different metaheuristic approaches are used to determine the appropriate adaptive neuro-fuzzy inference systems model parameters for short-term wind speed estimation, and analyzed comparatively. To validate the effectiveness of the proposed approach, wind speed series collected from a wind observation station located in Turkey are used in the short-term wind speed forecasting. In the first step, the results of analysis for finding the accurate model revealed that the optimal model that is proposed is adaptive neuro-fuzzy inference systems7-1 architecture. The meta-heuristic algorithms used in the optimization of adaptive neuro-fuzzy inference systems model parameters are then independently run 10 times, and the performance results are calculated statistically for the training and test phases of the adaptive neuro-fuzzy inference systems model. The results of the study clearly show that the adaptive neuro-fuzzy inference systems-particle swarm optimization hybrid model has the best performance in the training aspect, but it is observed that the ANFIS-differential evolution hybrid model gives better results than the others in the test step.

Cite this article as: E. Dokur, U. Yüzgeç, M. Kurban, "Performance Comparison of Hybrid Neuro-fuzzy Models using Meta-heuristic Algorithms for Short-term Wind Speed Forecasting" Electrica, vol. 21, no. 3, pp. 305-321, Sep. 2021.

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