*4.4. Discussion*

When evaluating the obtained results relative to the literature herein presented, the first important point to consider is that the higher frequency of acquisition provided by the current system does offer more, and useful, information for modeling PV generation at a plant level [8,35]. This better resolution, coupled with the acquisition strategy, serves to confirm what other works have shown, that higher resolutions are important for PV forecasting [19,35,51,52,59,60].

The results also show the suitability of neural networks for modeling the relationship between image data and PV power. Its use in applications such as determining cloud albedo [33] or optical depth [55] should aid in forecasting efforts. Due to the low cost of the equipment used and the key information it was able to provide, approaches that employ multiple imagers [51,55,60] should be more easily employed. The combination of high-fidelity regression, high-frequency data and low cost permits not only a higher accessibility for developing countries to endeavor in PV energy research, but also for a more complex and in-depth look into the PV forecast problem.

#### **5. Conclusions**

Validation was performed on the data selected during the correlation analysis by using a linear model as the baseline and a neural network regression as a nonlinear model. It was possible to model power variations with up to 60 s intervals based on the data acquired by the developed system. Both the characteristics of the data themselves and of the selected features used for training the neural network were proven relevant to the intra-minute solar forecast problem.

Given the high-accuracy results, the data frequency and chosen variables were deemed relevant for intra-minute forecasting. The acquisition by exception proved to yield data rich in information surrounding solar variability; however, the event structure should be redefined in order to more accurately translate the reality. Since, through the data analysis, a 15 to 60 s horizon was deemed ideal given the available data, and that assumption was validated by the neural network model, an event structure capable of fully encompassing this horizon is recommended. Based on the information provided by this experimental research, an event structure with 90 s prior to the point of detection and 30 s after it should be enough to provide a clearer view on the subject of study.

Through forecasting, renewable energy sources will become more reliable and help steer the energy paradigm into a less fossil-reliant reality. With the coupling of multihorizon forecasting, power electronics and energy storage systems, RES can lead to a new and clean energy era. To make this happen, more research into forecasting of the solar resource in different temporal and spatial scales is required, as well as the combination of forecasting with energy storage. The recommendations to improve upon the foundation laid by this work are as follows:


**Author Contributions:** Conceptualization, G.F.B., R.F.C. and C.H.B.; methodology, G.F.B., R.F.C. and C.H.B.; software, G.F.B.; validation, G.F.B., R.F.C. and C.H.B.; formal analysis, G.F.B., R.F.C. and C.H.B.; investigation, G.F.B.; resources, G.F.B., R.F.C. and C.H.B.; data curation, G.F.B.; writing original draft preparation, G.F.B.; writing—review and editing, G.F.B., R.F.C. and C.H.B.; visualization, G.F.B., R.F.C. and C.H.B.; supervision, R.F.C. and C.H.B.; project administration, R.F.C. and C.H.B.; funding acquisition, R.F.C. and C.H.B. All authors have read and agreed to the published version of the manuscript.

**Funding:** The authors thank for the financial support provided by the Brazilian funding agencies CNPq, CAPES, FINEP and FAPERJ. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance Code 001.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The data presented in this study are openly available in Mendeley Data at 10.17632/r83r6g5y6t.1, reference number [72].

**Conflicts of Interest:** The authors declare no conflict of interest.

### **References**

