A Review and Evaluation of the State of Art in Image-Based Solar Energy Forecasting: The Methodology and Technology Used  
Carlos M. Travieso-Gonzalez  
1  
Fidel Cabrera-Quintero  
1  
Alejandro Pinan-Roescher  
1  
Sergio Celada-Bernal  
1  
Digital Signal Processing Division, Institute for Technological Development and Innovation in Communications (IDEITC), University of Las Palmas de Gran Canaria (ULFOC), 35017 Las Palmas de Gran Canaria, Spain; fidel.cabrera@ulpges.es (F.C-Q.); alejandro.pinan@ulpgs.es (A.P.-R.)  
1  
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Footnote 2: [[https://www.mdpi.com/journals/pdf/](https://www.mdpi.com/journals/pdf/)]([https://www.mdpi.com/journals/pdf/](https://www.mdpi.com/journals/pdf/))  
The energy sector has various meteorological service requirements to support decision-making processes, including day-to-day operations and long-term strategic planning. This makes logical sense given the complexity of the industry.  
With the increasing share of solar power in the overall electricity grid, power plants face a difficult task trying to balance energy generation and consumption. This challenge is compounded by the ongoing promotion of sustainable and renewable energy sources by governments and organizations in response to the current energy crisis caused by excessive and uncontrolled fossil fuel consumption. To mitigate the negative effects of carbon dioxide emissions into the atmosphere, it is crucial for the energy sector to find innovative solutions to this problem.  
Renewable power generation and usage, unlike fossil fuels, involves lower emissions; however, one disadvantage of renewable energy sources is their unstable nature [1].  
The transient nature of renewable energy sources is exemplified by the supply fluctuations of solar energy caused by variable cloud conditions. To address this issue, recent research has explored the development of an irradiance-based weather derivative to hedge against cloud risk [2].  
To use solar energy more efficiently, it is important for photovoltaic grid operators to have successful mechanisms that enable accurate, short-term forecasting of the amount of power produced by solar panels. Such forecasting is critical for the efficient scheduling and dispatch of distributed energy resources (DERs).  
This report aims to address the issue of solar power forecasting by examining the evolution of techniques and technologies employed by various researchers. The studies reviewed are primarily focused on addressing the challenges and motivations previously mentioned.  
Section 2 examines various radiation detectors used for forecasting solar irradiance. These detectors are classified based on the prediction time horizon they can cover. In Section 3, research developments in this field are presented based on different prediction methods employed. This includes a diverse range of approaches in detection algorithms and cloud segmentation and classification, as well as feature extraction and post-processing. Finally, Sections 4 and 5 provide discussions and conclusions resulting from the study conducted in the preceding sections.  
The main focus of this paper is to discuss the innovation of very short-term spatial-temporal irradiance forecasting, including forecasting periods that are even shorter than the satellites' orbital period. To do so, this study utilizes various forecasting techniques that implement sky images captured from the Earth's surface via all-sky cameras.  
To provide readers with a concise and visual summary of the progress made in this field, significant aspects, techniques, and achievements obtained from different studies are presented in a series of tables. These tables are conveniently summarized in Appendix A.