## 5 Conclusions  
This article provides an updated review of the techniques and technologies related to solar forecasting research. More specifically, this article focuses on the very short-range forecasting method that utilizes sky imagery captured by all-sky cameras from the Earth's surface. Indeed, this method offers significant potential for further research in the field of stabilization and optimization in the photovoltaic solar power sector. The article compares the different approaches for solar forecasting in terms of the forecasting method, technologies employed, spatio-temporal horizons to be covered, the measurement error, and the benchmark model. The various modalities of techniques are classified according to their methodological and technological fields. Their respective advantages and disadvantages are also examined.  
In order to provide a comprehensive assessment, research methods based on irradiance measuring sensors are evaluated separately from those employing satellite and all-sky camera techniques. Additionally, a distinction is made between investigations that employ statistical and deep learning methods versus those that use numerical models. Another differentiation is made as well as with those utilizing satellite images or ground-based cameras. Furthermore, comparisons are drawn between studies that utilize a combination of these techniques and the instrumentation in hybrid systems.  
In terms of equipment, the current high-resolution cameras are low-cost, offer high temporal and spatial resolution, and are effective for short-range irradiance forecasting compared to satellite images. They require less infrastructure, making them promising solutions for addressing the current imbalance in solar power generation by accurately predicting future PV generation outage events.  
While there are many techniques available and the forecasting results are encouraging, there are still areas for improvement. Difficulties that continue to plague the field of solar forecasting are mainly due to clouds. Techniques for processing thin clouds such as fog, handling cloudy skies, dissolution, formation from other clouds, and dealing with cases of multi-layer clouds still need to be better developed. In such cases, hybrid systems have shown significant advances. Nonetheless, there is a lack of articles that calculate cloud height by stereoscopic methods to project cloud shadows on the surface and provide irradiance maps. Therefore, the future of solar forecasting lies in combined approachesthat control the non-linearity of cloud processes, refine accuracy by correctly estimating background errors, and ultimately contribute to optimal solar power generation. In the end, advancing such approaches will alleviate the current energy crisis.  
Conceptualization, C.M.T.-G., F.C.-Q., A.P.-R. and S.C.-B.; methodology, C.M.T.-G., F.C.-Q., A.P.-R. and S.C.-B.; software, C.M.T.-G., F.C.-Q., A.P.-R. and S.C.-B.; validation, C.M.T.-G., F.C.-Q., A.P.-R. and S.C.-B.; formal analysis, C.M.T.-G., F.C.-Q., A.P.-R. and S.C.-B.; investigation, C.M.T.-G., F.C.-Q., A.P.-R. and S.C.-B.; resources, C.M.T.-G., F.C.-Q., A.P.-R. and S.C.-B.; data curation, C.M.T.-G., F.C.-Q., A.P.-R. and S.C.-B.; writing--original draft preparation, C.M.T.-G., F.C.-Q., A.P.-R. and S.C.-B.; writing--review and editing, C.M.T.-G., F.C.-Q., A.P.-R. and S.C.-B.; visualization, C.M.T.-G., F.C.-Q., A.P.-R. and S.C.-B.; supervision, C.M.T.-G., F.C.-Q., A.P.-R. and S.C.-B.; project administration, C.M.T.-G., F.C.-Q., A.P.-R. and S.C.-B.; funding acquisition, C.M.T.-G., F.C.-Q., A.P.-R. and S.C.-B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
This work is supported under Grant CEI2021-06, from direct agreement SD-21/08 by Consejeria de Economia, Industria, comercio y conocimiento from Gobierno de Canarias to ULPGC.  
The authors declare no conflicts of interest.