# Evaluation and Comparison of Spatial Clustering for Solar Irradiance Time Series
## 7 Conclusions and Perspectives
Clustering allows us to perform learning on data measured on one or several sites with similar characteristics and then to perform transfer learning to be able to make a forecast on the new site;
* To make GHI predictions on a site where few historical measurements are available. Clustering allows us to increase the number of training data (and thus the quality of the training by avoiding overfitting) by integrating data from other sites of the same cluster;
* To make prediction on a site where no measurement device is available. Clustering coupled with Kriging could allow for making a forecast from measurements obtained on nearby sites with similar meteorological characteristics;
* To improve a forecast by integrating during the learning process time-delayed data from other sites in the same cluster. In all the previous cases, and based on the results shown previously, the authors propose to use k-means clustering or hierarchical clustering interchangeably (as many clusters as Koppen classes) based on mean, Hurst exponent, and forecastability computed from clear-sky index series (derived by CAMs estimation). However, for those who are less eager to obtain satisfactory results, the possibility of using the Koppen classification (recalculated for the time series study period) remains possible. It allows a classification that can be described as naive. This paper is the collaborative work of all authors. Conceptualization, L.G.-G. and C.V.; methodology, L.G.-G. and C.V.; software, L.G.-G.; validation, L.G.-G., C.V. and G.N. ; investigation, L.G.-G.; resources, J.A. ; data curation, L.G.-G.; writing--original draft preparation, L.G.-G.; writing--review and editing, C.V. and G.N. ; supervision, C.V. and G.N. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This paper is the collaborative work of all authors. Conceptualization, L.G.-G. and C.V.; methodology, L.G.-G. and C.V.; software, L.G.-G.; validation, L.G.-G., C.V. and G.N. ; investigation, L.G.-G.; resources, J.A. ; data curation, L.G.-G.; writing--original draft preparation, L.G.-G.; writing--review and editing, C.V. and G.N. ; supervision, C.V. and G.N. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The authors have not provided data associated with this article. **Acknowledgments:** The authors would like to thank (i) the network of agroclimatic stations of the Spanish Ministry of Agriculture, Food, and Environment, specifically the agrometeorological stations network of SIAR for the information provided on solar irradiance ([[https://eportal.mapa.gob.es](https://eportal.mapa.gob.es)]([https://eportal.mapa.gob.es](https://eportal.mapa.gob.es))) (accessed on 2 June 2021; and (ii) the atmospheric monitoring service CAMs-McClear for the information on solar irradiance under clear sky conditions ([[http://www.soda-pro.com](http://www.soda-pro.com)]([http://www.soda-pro.com](http://www.soda-pro.com))) (accessed on 10 March 2021). **Conflicts of Interest:** The authors declare no conflict of interest.