Application of interval type-2 TSK FLS method based on IGWO algorithm in short-term photovoltaic power forecasting

Abstract

For short-term PV power prediction, based on interval type-2 Takagi-Sugeno-Kang fuzzy logic systems (IT2 TSK FLS), combined with improved grey wolf optimizer (IGWO) algorithm, an IGWO-IT2 TSK FLS method was proposed. Compared with the type-1 TSK fuzzy logic system method, interval type-2 fuzzy sets could simultaneously model both intra-personal uncertainty and inter-personal uncertainty based on the training of the existing error back propagation (BP) algorithm, and the IGWO algorithm was used for training the model premise and consequent parameters to further improve the predictive performance of the model. By improving the gray wolf optimization algorithm, the early convergence judgment mechanism, nonlinear cosine adjustment strategy, and Levy flight strategy were introduced to improve the convergence speed of the algorithm and avoid the problem of falling into local optimum. The interval type-2 TSK FLS method based on the IGWO algorithm was applied to the real-world photovoltaic power time series forecasting instance. Under the same conditions, it was also compared with different IT2 TSK FLS methods, such as type I TSK FLS method, BP algorithm, genetic algorithm, differential evolution, particle swarm optimization, biogeography optimization, gray wolf optimization, etc. Experimental results showed that the proposed method based on IGWO algorithm outperformed other methods in performance, showing its effectiveness and application potential.

Keywords

photovoltaic power / interval type-2 fuzzy logic system / grey wolf optimizer algorithm / forecast performance of model

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