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Solar Power Prediction Using Interval Type-2 TSK Modeling

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3 Author(s)
Jafarzadeh, S. ; Comput. & Electr. Eng. & Comput. Sci. Dept., California State Univ. Bakersfield, Bakersfield, CA, USA ; Fadali, M.S. ; Evrenosoglu, C.Y.

The random nature of solar energy resources is one of the obstacles to their large-scale proliferation in power systems. The most practical way to predict this renewable source of energy is to use meteorological data. However, such data are usually provided in a qualitative form that cannot be exploited using traditional quantitative methods but which can be modeled using fuzzy logic. This paper proposes type-1 and interval type-2 Takagi-Sugeno-Kang (TSK) fuzzy systems for the modeling and prediction of solar power plants. The paper considers TSK models with type-1 antecedents and crisp consequents, type-1 antecedents and consequents, and type-2 antecedents and crisp consequents. The design methodology for tuning the antecedents and consequents of each model is described. Finally, input-output data sets from a solar plant are used to obtain the three TSK models and their prediction results are compared to results from the literature. The results show that type-2 TSK models with type2 antecedents and crisp consequents provide the best performance based on the solar plant data.

Published in:

Sustainable Energy, IEEE Transactions on  (Volume:4 ,  Issue: 2 )