In the last two decades, renewable energy has been paid immeasurable attention to toward the attainment of electricity requirements for domestic, industrial, and agriculture sectors. Solar forecasting plays a vital role in smooth operation, scheduling, and balancing of electricity production by standalone PV plants as well as grid interconnected solar PV plants. Numerous models and techniques have been developed in short, mid and long-term solar forecasting. This paper analyzes some of the potential solar forecasting models based on various methodologies discussed in literature, by mainly focusing on investigating the influence of meteorological variables, time horizon, climatic zone, pre-processing techniques, air pollution, and sample size on the complexity and accuracy of the model. To make the paper reader-friendly, it presents all-important parameters and findings of the models revealed from different studies in a tabular mode having the year of publication, time resolution, input parameters, forecasted parameters, error metrics, and performance. The literature studied showed that ANN-based models outperform the others due to their nonlinear complex problemsolving capabilities. Their accuracy can be further improved by hybridization of the two models or by performing pre-processing on the input data. Besides, it also discusses the diverse key constituents that affect the accuracy of a model. It has been observed that the proper selection of training and testing period along with the correlated dependent variables also enhances the accuracy of the model.

在过去的二十年中，可再生能源在实现家庭，工业和农业部门的电力需求方面受到了不可估量的关注。太阳能预报在独立光伏电站以及与电网相连的太阳能光伏电站的平稳运行，调度和电力生产的平衡中起着至关重要的作用。在短期，中期和长期的太阳预报中已经开发了许多模型和技术。本文主要基于调查气象变量，时间范围，气候带，预处理技术，空气污染和样本量对复杂性和影响的影响，基于文献讨论的各种方法，分析了一些潜在的太阳预报模型。模型的准确性。为了使纸张易于阅读，它以表格的形式显示了所有研究的重要参数和模型发现，这些模型具有发布年份，时间分辨率，输入参数，预测参数，误差指标和性能。研究文献表明，基于ANN的模型具有非线性复杂问题解决能力，因此其性能优于其他模型。通过两种模型的混合或对输入数据进行预处理，可以进一步提高其准确性。此外，它还讨论了影响模型准确性的各种关键要素。已经观察到，正确选择训练和测试时间以及相关的因变量也可以提高模型的准确性。