# A New Index Assessing the Viability of PAR Application Projects Used to Validate PAR Models
## 5 Conclusions  
The new index allows the assessment of the viability of _PAR_ projects, as it is based on exceedance probabilities, unlike the rest of the indices, which estimate the closeness of the model to the observed data point by point. Indeed, a good model according to these indices must also be representative of the degree of viability of the project, but it is possible that a bad model according to these indices turns out to adequately represent that viability. This is the case shown in the work, where Model 8 is the best model according to EPI, but it is one of the worst according to the rest of the indices. EPI adds information from a different point of view to the traditional statistics indices and could help in decision making to assess the suitability of _PAR_ resources in an arbitrary location. EPI could be also useful for discerning which _PAR_ model is more appropriate to estimate _PAR_ in order to assess its potential regarding industrial exploitations such as agro-food cultivation, vegetables for biomass cultivation, or micro-algae cultivation to obtain biofuels. A methodology for developing regional hourly models from 3 h satellite data was presented as well. The clustering utilizing k-means methods led to a division of the field of study into two regions with similar features regarding _PAR_. For each of these two regions, a set of nine multilinear regression and nine artificial neural network models was developed and validated. As _PAR_ is among the main variables to estimate the growth of biomass and microalgae, these models were validated bearing in mind that the viability of _PAR_ projects can be applied to a priori estimate biomass production or the viability of a microalgae growth in different regions across mainland Spain, for instance. The clustering division of the territory fitted in with the two main climatic areas of Spain. This information will be useful in futures studies to assess the best species of microalgae cultivation according to the level of _PAR_ irradiance received and the most suitable region to set it up. The results showed small differences in the best models between multilinear regressions and artificial neural networks, but the higher time consumption of artificial neural networks made the multilinear regression models a better option. For the first ranking  
Figure 6: Comparison between Models 1 and 8 against bibliography models. considered--indices which estimate the closeness of the model to the observed data point by point--all the models that did not include _GHI_ as an input obtained the worst results, remarking the importance of _GHI_ for _PAR_ modeling in this case (the best performance was reached by Model 1, which only utilizes _GHI_ as input). However, in the case of assessing the representativeness of a model when estimating the viability of a _PAR_ project, _GHI_ is not required explicitly for the model (Model 8), but indeed implicitly throughout \(k_{t}\). This result illustrates that a model with traditional statistics, which can appear to be a bad model in the sense of replicating ground measurements, can be a good model from the point of view of the exceedance probabilities and may add valuable information for industrial exploitation. A correction using a site-adaptation technique was introduced. The statistical results of the site adaptation to Model 1 and Model 8 improved some statistics but got worse in others. Thus, there was no clear improvement in the use of site adaptation in this case. Finally, a comparison between Model 1 and 8 against four models of the bibliography was carried out. The results of the comparison showed that the performance of the models presented in this work was better than those of the bibliography, though the slopes and the R\({}^{2}\) of Model 8 and the model proposed in [41] were similar, and the intercepts of the model proposed in [41] were the lowest. Conceptualization, R.X.V., J.M.V. and F.F.-C.; methodology, J.M.V. ; software, F.F.-C.; validation, F.F.-C.; formal analysis, R.X.V., J.M.V.