Solar Power Prediction Using Interval Type-2 TSK Modeling

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 explo...

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Bibliographic Details
Published in:IEEE transactions on sustainable energy 2013-04, Vol.4 (2), p.333-339
Main Authors: Jafarzadeh, Saeed, Fadali, M. Sami, Evrenosoglu, Cansın Yaman
Format: Article
Language:English
Subjects:
Data models
Fuzzy logic
Fuzzy sets
Interval type-2 fuzzy model
Power generation
Pragmatics
Predictive models
Solar energy
solar power prediction
Takagi-Sugeno-Kang (TSK) fuzzy model
Uncertainty
Weather forecasting
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Summary: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.
ISSN:1949-3029
1949-3037
DOI:10.1109/TSTE.2012.2224893