Abdelghafar Sara, Alshater Heba, Abouelmagd Lobna M, Darwish Ashraf, Hassanien Aboul Ella
School of Computer Science, Canadian International College (CIC), Cairo, Egypt.
Department of Forensic Medicine and Clinical Toxicology, Menoufia University Hospital, Shebin El-Kom, Egypt.
Sci Rep. 2025 Sep 16;15(1):32559. doi: 10.1038/s41598-025-18632-4.
Over the decades, as industrialization progressed, energy has been a critical topic for scientists and engineers. Particularly, photovoltaic technology has drawn great attention in the renewable energy industry as an environmentally clean technology for converting sunlight into electricity. However, the complexity of energy chemistry and the need for novel materials to improve solar cell efficiency and cost-effectiveness have led to challenges in establishing rules beyond empirical observations. Machine learning models are being developed to streamline the prediction process and efficiently predict photovoltaic parameters. This paper proposes a novel hybrid-optimized multi-objective predictive model to predict the photovoltaic parameters: open-circuit voltage (Voc), current density (Jsc), fill factor (FF), and power conversion efficiency (PCE). The proposed model is based on Bayesian Optimization (BO) with the ensemble Bootstrap Aggregating (Bagging) decision tree. The proposed model integrates with the Explainable Artificial Intelligence (XAI) using the SHAP (Shapley Additive Explanations) values to introduce feature importance analysis that provides valuable insights into the impact of individual features on prediction outputs. The proposed model, named BO-Bagging, achieves high prediction accuracy, with an average high correlation coefficient of r = 0.92, a coefficient of determination of R = 0.82, and a Mean Square Error (MSE) of 0.00172. In terms of complexity, the BO-Bagging model has a short processing time that is indicated with an average training time of 182.7 s and an average inference time averaging 0.00062 s. Also, the number of predicted observations per second is measured by prediction speed, which results in good prediction accuracy with an average of 2188.4 and model size with an average of 10,740.4 KB. Finally, the proposed model's primary critical operations across each phase, from training to predicting the final outputs, are represented by 108 floating-point operations per second (FLOPS). All of these results demonstrate the proposed model's accuracy and high efficiency in intelligent chemical applications.
几十年来，随着工业化的发展，能源一直是科学家和工程师们关注的关键话题。特别是，光伏技术作为一种将阳光转化为电能的环境清洁技术，在可再生能源行业备受关注。然而，能源化学的复杂性以及对新型材料以提高太阳能电池效率和成本效益的需求，导致在建立超越经验观察的规则方面面临挑战。机器学习模型正在被开发以简化预测过程并有效预测光伏参数。本文提出了一种新颖的混合优化多目标预测模型来预测光伏参数：开路电压（Voc）、电流密度（Jsc）、填充因子（FF）和功率转换效率（PCE）。所提出的模型基于贝叶斯优化（BO）与集成自助聚合（Bagging）决策树。所提出的模型使用SHAP（Shapley值加法解释）值与可解释人工智能（XAI）集成，以引入特征重要性分析，该分析提供了关于单个特征对预测输出影响的有价值见解。所提出的模型名为BO - Bagging，具有很高的预测准确性，平均高相关系数r = 0.92，决定系数R = 0.82，均方误差（MSE）为0.00172。在复杂性方面，BO - Bagging模型处理时间短，平均训练时间为182.7秒，平均推理时间平均为0.00062秒。此外，每秒预测观测数由预测速度衡量，平均为2188.4时预测准确性良好，模型大小平均为10740.4KB。最后，所提出模型从训练到预测最终输出的每个阶段的主要关键操作由每秒108次浮点运算（FLOPS）表示。所有这些结果都证明了所提出模型在智能化学应用中的准确性和高效性。
