Accurate solar radiation (SR) prediction is one of the essential prerequisites of harvesting solar energy. The current study proposed a novel intelligence model through hybridization of Adaptive Neuro-Fuzzy Inference System (ANFIS) with two metaheuristic optimization algorithms, Salp Swarm Algorithm (SSA) and Grasshopper Optimization Algorithm (GOA) (ANFIS-muSG) for global SR prediction at different locations of North Dakota, USA. The performance of the proposed ANFIS-muSG model was compared with classical ANFIS, ANFIS-GOA, ANFIS-SSA, ANFIS-Grey Wolf Optimizer (ANFIS-GWO), ANFIS-Particle Swarm Optimization (ANFIS-PSO), ANFIS-Genetic Algorithm (ANFIS-GA) and ANFIS-Dragonfly Algorithm (ANFIS-DA). Consistent maximum, mean and minimum air temperature data for nine years (2010–2018) were used to build the models. ANFIS-muSG showed 25.7%–54.8% higher performance accuracy in terms of root mean square error compared to other models at different locations of the study areas. The model developed in this study can be employed for SR prediction from temperature only. The results indicate the potential of hybridization of ANFIS with the metaheuristic optimization algorithms for improvement of prediction accuracy.

中文翻译：

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使用气温预测北达科他州的全球太阳辐射：开发新型混合智能模型

准确的太阳辐射（SR）预测是收集太阳能的重要先决条件之一。当前的研究通过将自适应神经模糊推理系统（ANFIS）与两种元启发式优化算法（Salp Swarm Algorithm（SSA）和Grasshopper Optimization Algorithm（GOA）（ANFIS-muSG）相结合）提出了一种新颖的智能模型，用于不同条件下的全局SR预测。美国北达科他州的地点。所提出的ANFIS-muSG模型的性能与经典ANFIS、ANFIS-GOA、ANFIS-SSA、ANFIS-灰狼优化器（ANFIS-GWO）、ANFIS-粒子群优化（ANFIS-PSO）、ANFIS-遗传算法（ANFIS-遗传算法）进行了比较。 ANFIS-GA）和ANFIS-蜻蜓算法（ANFIS-DA）。九年来（2010-2018 年）一致的最高、平均和最低气温数据用于构建模型。与研究区域不同位置的其他模型相比，ANFIS-muSG 在均方根误差方面表现出更高的性能精度 25.7%–54.8%。本研究中开发的模型可用于仅根据温度进行 SR 预测。结果表明，ANFIS 与元启发式优化算法的混合具有提高预测精度的潜力。