Hybrid Photovoltaic Output Forecasting Model with Temporal Convolutional Network Using Maximal Information Coefficient and White Shark Optimizer

Xilong Lin, Yisen Niu, Zixuan Yan, Lianglin Zou, Ping Tang and Jifeng Song ()
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Xilong Lin: School of New Energy, North China Electric Power University, Beijing 102206, China
Yisen Niu: School of New Energy, North China Electric Power University, Beijing 102206, China
Zixuan Yan: School of New Energy, North China Electric Power University, Beijing 102206, China
Lianglin Zou: School of New Energy, North China Electric Power University, Beijing 102206, China
Ping Tang: School of New Energy, North China Electric Power University, Beijing 102206, China
Jifeng Song: Institute of Energy Power Innovation, North China Electric Power University, Beijing 102206, China

Sustainability, 2024, vol. 16, issue 14, 1-20

Abstract: Accurate forecasting of PV power not only enhances the utilization of solar energy but also assists power system operators in planning and executing efficient power management. The Temporal Convolutional Network (TCN) is utilized for feature extraction from the data, while the White Shark Optimization (WSO) algorithm optimizes the TCN parameters. Given the extensive dataset and the complex variables influencing PV output in this study, the maximal information coefficient (MIC) method is employed. Initially, mutual information values are computed for the base data, and less significant variables are eliminated. Subsequently, the refined data are fed into the TCN, which is fine-tuned using WSO. Finally, the model outputs the prediction results. For testing, one year of data from a dual-axis tracking PV system is used, and the robustness of the model is further confirmed using data from single-axis and stationary PV systems. The findings demonstrate that the MIC-WSO-TCN model outperforms several benchmark models in terms of accuracy and reliability for predicting PV power.

Keywords: photovoltaic power forecasting; maximal information coefficient; temporal convolutional network; white shark optimizer (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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