A review and evaluation of solar forecasting technologies. (2021)

• Record Type: Journal Article
• Authors: Gupta, Anuj; Gupta, Kapil; Saroha, Sumit
• Is Part Of: Materials today. Volume 47:Part 10(2021)
• Page Start: 2420
• Page End: 2425
• Publication Date: 2021
• Subjects: Error Metrics -- Neural network -- Forecasting techniques -- Markov Chain -- Numerical Weather Prediction -- Support vector machine; Materials science -- Congresses -- Periodicals; 620.1
• DOI: 10.1016/j.matpr.2021.04.491
• Languages: English
• ISSNs: 2214-7853

Abstract

Abstract: After the oil crisis in 1973, the planet has to believe in alternate sources of energy aside from conventional energy resources. Among different renewable resources, solar energy is one of the most famous and attractive suppliers of electric power generation due to zero emission of CO2 . However, the forecasting of a solar component in advance is one of the crucial task due to the uncertain behavior of climatic parameters. This paper provides an analysis of the techniques used in the literature to forecast solar irradiance. The study's main goal is to see how meteorological input parameters, time horizons, pre-processing methodology, optimization, and sample size affect the model's complexity and accuracy. Different important parameters & findings of studies are presented in tabular mode. The paper provides key findings based on studied literature to select the optimal model for a specific site. This paper also discusses the metrics used to measure the efficiency of the forecasted model.