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Hybrid Approach of Fractional Generalized Pareto Motion and Cosine Similarity Hidden Markov Model for Solar Radiation Forecasting

Authors :
Wanqing Song
Wujin Deng
Dongdong Chen
Rong Jin
Aleksey Kudreyko
Source :
Fractal and Fractional; Volume 7; Issue 1; Pages: 93
Publication Year :
2023
Publisher :
Multidisciplinary Digital Publishing Institute, 2023.

Abstract

Power from solar energy is not reliable, due to weather-related factors, which diminishes the power system’s reliability. Therefore, this study suggests a way to predict the intensity of solar irradiance using various statistical algorithms and artificial intelligence. In particular, we suggest the use of a hybrid predictive model, combining statistical properties and historical data training. In order to evaluate the maximum prediction steps of solar irradiance, the maximum Lyapunov exponent was applied. Then, we used the cosine similarity algorithm in the hidden Markov model for the initial prediction. The combination of the Hurst exponent and tail parameter revealed the self-similarity and long-range dependence of the fractional generalized Pareto motion, which enabled us to consider the iterative predictive model. The initial prediction was substituted into a stochastic differential equation to achieve the final prediction, which prevents error propagation. The effectiveness of the hybrid model was demonstrated in the case study.

Details

Language :
English
ISSN :
25043110
Database :
OpenAIRE
Journal :
Fractal and Fractional; Volume 7; Issue 1; Pages: 93
Accession number :
edsair.doi.dedup.....c81f96ec93d8ca02a9fc4828eb562443
Full Text :
https://doi.org/10.3390/fractalfract7010093