Modeling optimization method based on Gamma test and NSGA II for forecast of PV power output

ABSTRACT

Forecasting can overcome the shortcomings of volatility and intermittent nature of photovoltaic (PV) power. The PV power output is affected by many factors. To improve the forecasting accuracy, the forecasting model needs considering as many relevant factors as possible, but this makes the input feature vector contain redundant information and leads to high complexity forecasting algorithm, low system stability, and poor application practicability. In this paper, we develop a novel optimization method based on Gamma test and Non-dominated Sorting Genetic Algorithm to optimize the input feature vector space and reduce the information redundancy. Meanwhile, to build targeted models for different weather conditions, a new weather variability and smoothed clearness index are deduced in this paper to measure the weather fluctuation. Based on them, the weather is classified into A and B microweather types. Two sets of input vector for the two types are optimized, and only half of the impact factors (21/51) are selected by this optimization method. Two sets of support vector machine, ELMAN artificial neural network and multivariate autoregressive models, are built. The method can greatly simplify the computational complexity as well as improve the accuracy and efficiency of the models.

KEYWORDS:

• PV output forecasting
• gamma test
• NSGA-II
• impacting factors
• dimensionality reduction

Acknowledgments

The authors would like to thank Dr. Yonghui Li, the professor of School of Electrical & Information Engineering and the director of Wireless Engineering Laboratory, the University of Sydney, for his valuable comments and suggestions.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by the Zhejiang Province key R&D project [2021C01112]; the National Key Research and Development Program of China [2017YFA0700301].