论文标题
自适应神经模糊的推理系统和多层感知器模型，该模型接受了灰狼优化器训练，可预测太阳弥漫性分数
Adaptive Neuro-Fuzzy Inference System and a Multilayer Perceptron Model Trained with Grey Wolf Optimizer for Predicting Solar Diffuse Fraction
论文作者
论文摘要
太阳能扩散分数（DF）的准确预测有时称为扩散比，是太阳能研究的重要主题。在本研究中，讨论了弥漫性辐照研究的当前状态，然后使用来自西班牙阿尔梅里亚的大型数据集（将近8年）的大量数据集（将近8年）进行了研究。本文使用的ML模型是基于混合自适应网络的模糊推理系统（ANFIS），单层多层感知器（MLP）和混合多层多层感知器感知器 - 绿色狼型狼优化器（MLP-GWO）。使用西班牙的各种太阳和弥散分数（DF）辐射率数据评估了这些模型的预测精度。然后使用两个经常使用的评估标准，平均绝对误差（MAE）和均方根误差（RMSE）评估结果。结果表明，在训练和测试程序中，MLP-GWO模型随后是ANFIS模型，提供了更高的性能。
The accurate prediction of the solar Diffuse Fraction (DF), sometimes called the Diffuse Ratio, is an important topic for solar energy research. In the present study, the current state of Diffuse Irradiance research is discussed and then three robust, Machine Learning (ML) models, are examined using a large dataset (almost 8 years) of hourly readings from Almeria, Spain. The ML models used herein, are a hybrid Adaptive Network-based Fuzzy Inference System (ANFIS), a single Multi-Layer Perceptron (MLP) and a hybrid Multi-Layer Perceptron-Grey Wolf Optimizer (MLP-GWO). These models were evaluated for their predictive precision, using various Solar and Diffuse Fraction (DF) irradiance data, from Spain. The results were then evaluated using two frequently used evaluation criteria, the Mean Absolute Error (MAE) and the Root Mean Square Error (RMSE). The results showed that the MLP-GWO model, followed by the ANFIS model, provided a higher performance, in both the training and the testing procedures.
