Classification and Summarization of Solar Irradiance and Power Forecasting Methods: A Thorough Review

卷:9

Review

关键词: ARTIFICIAL NEURAL-NETWORK;    SUPPORT VECTOR MACHINE;    EXTREME LEARNING-MACHINE;    NUMERICAL WEATHER PREDICTION;    PRINCIPAL COMPONENT ANALYSIS;    PARTICLE SWARM OPTIMIZATION;    SLIDING-MODE CONTROL;    TOTAL SKY IMAGER;    HYBRID MODEL;    RADIATION PREDICTION;   

DOI  :  10.17775/CSEEJPES.2020.04930

来源: SCIE

【 摘 要 】

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.