Aging Life Prediction Method for Ethylene-Vinyl Acetate Encapsulant Film Based on Chaotic Sparrow Search Optimization Algorithm and Ensemble Learning

Engineering notes

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Chinese explanation / 中文解读

中文解读待补充：本站会优先为光伏效率、钙钛矿太阳能电池、储能技术、太阳能热利用、BIPV、并网技术等高价值论文补充中文说明。

Original abstract

Predicting the aging life of Ethylene-vinyl Acetate Copolymer encapsulant film has important value for photovoltaic systems. It helps reveal potential quality problems in advance and provides scientific support for the operation and maintenance of photovoltaic power stations. It also improves the overall reliability and power generation efficiency of photovoltaic systems. To address the insufficient prediction accuracy and low efficiency of existing aging life prediction models, an innovative prediction model is proposed. The model is based on the Chaos Sparrow Search Optimization Algorithm (CSSOA) and ensemble learning to enhance prediction accuracy and efficiency for ethylene vinyl acetate copolymer encapsulation film. This model leverages the parameter optimization capability of the algorithm and the strong generalization of ensemble learning to optimize algorithm parameters while integrating the prediction outputs of multiple base learners. It predicts the aging life of the encapsulant film with high precision. The experimental results show that the research model achieved prediction accuracies of 97.1%, 97.2%, and 95.6% for the three key aging life prediction indicators: crosslinking degree, transmittance, and tensile strength. These accuracies are significantly higher than those of the comparative model. The degree of crosslinking directly reflects the chemical stability and aging degree of the adhesive film. The light transmittance determines the photoelectric conversion efficiency of the photovoltaic module. The tensile strength ensures the mechanical reliability of the packaging structure. Together, these three factors constitute the core basis for evaluating the degradation status of EVA packaging adhesive film. When the data size reaches 200, the model uses only 512.3 MB of memory and has a response time of 958.7 ms. These results demonstrate that the model has clear advantages in prediction accuracy and efficiency and meets the needs of photovoltaic power station management.

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