Solar forecasting is of great importance for ensuring safe and stable operations of the power system with increased solar power integration

thus numerous models have been presented and reviewed to predict solar irradiance and power forecasting in the past decade. Nevertheless

few studies take into account the temporal and spatial resolutions along with specific characteristics of the models. Therefore

this paper aims to demonstrate a comprehensive and systematic review to further solve these problems. First

five classifications and seven pre-processing methods of solar forecasting data are systematically reviewed

which are significant in improving forecasting accuracy. Then

various methods utilized in solar irradiance and power forecasting are thoroughly summarized and discussed

in which 128 algorithms are elaborated in tables in the light of input variables

temporal resolution

spatial resolution

forecast variables

metrics

and characteristics for a more fair and comprehensive comparison. Moreover

they are categorized into four groups

namely

statistical

physical

hybrid

and others with relevant application conditions and features. Meanwhile

six categories

along with 30 evaluation criteria

are summarized to clarify the major purposes/applicability of the different methods. The prominent merit of this study is that a total of seven perspectives and trends for further research in solar forecasting are identified

which aim to help readers more effectively utilize these approaches for future in-depth research.