# Photovoltaic Prediction Software: Evaluation with Real Data from Northern Spain  
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Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
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.mdpi.com/journals/pdf/1009069  
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software tools exist for this purpose. With these software tools, we can calculate the energy production of a desired system, by simply computing the system sizing parameters. Numerous reviews have focused on computer tools for PV system modeling and performance estimation. In this regard, [PERSON] et al. [2] presented a complete review of existing computer tools for analyzing the integration of renewable energy projects. Some of these software tools are extensively used as the basis for many scientific studies. For example, the work of [PERSON] et al. [3] included a comprehensive review of the software tools used in various studies for the simulation of PV systems. Undoubtedly, the performance of such PV tools determines the quality of the results obtained in the above-mentioned works. However, few studies have assessed the performance of different available PV simulation software tools against real-world data of PV system energy production. [PERSON] et al. [4] validated four PV tools (PVsyst, Hybrid Optimization Model for Multiple Energy Resources \"HOMER\", RETScreen and, System Model Advisor \"SAM\" Sunny Design), based on actual production data measured at six PV systems at the Desert Knowledge Australia Solar Centre (DKASC), a solar technology demonstration facility located at Alice Springs (Australia). Its systems were composed of modules of different PV technologies, i.e., monocrystalline, polycrystalline, amorphous silicon, and cadmium telluride (CdTe) thin-film modules. The installed power of the PV arrays ranged from 5.1 kW to 7 kW. The modeling tools were found to be relatively accurate, with annual total average percentage differences between \(-1.44\%\) and \(-4.93\%\), depending on the type of installation. HOMER and RETScreen, in particular, provided the most accurate results, while PVsyst generally provided conservative results. Although, Sunny was the simplest of the models and provided the least accurate results. [PERSON] et al. [5] analyzed the performance of six software tools (Transient System Simulation Tool \"TRNSYS\", Archelios, Polysun, PVsyst, PV*SOL, and Photovoltaic Geographical Information System \"PVGIS\") using the real electrical energy generated by a grid-connected 19.8 kWp photovoltaic installation located in Thrace (Greece). Their results revealed that all tools tended to overestimate the global irradiation received by the PV modules, but significantly underestimated the electrical energy generated by the installation. Specifically, the tool that showed the best overall performance was TRNSYS followed by Archelios. The results also showed that software based on or depending on the PVGIS irradiation database could be significantly inaccurate, especially if the study is performed for specific months or short-time periods. [PERSON] et al. [6] validated four popular PV modelling tools (SAM, PVWatts, PVsyst, and PV*SOL Expert) against actual data of nine photovoltaic systems, which included three utility-scale systems and six commercial-scale systems. In their study, they expanded the results of previous work [7] in which only the SAM tool was evaluated. Regarding the annual performance of the tools, relative errors ranged from 1.4% to \(-16.2\%\). In this case, PVWatts emerged as an outlier for the nine systems. Apart from the outliers, all annual errors were within 8%. On the hourly scale, apart from two specific outliers all root mean square errors (RMSE) were less than 7% for all tools and all systems.