# Enhanced Solar Photovoltaic System Management and Integration: The Digital Twin Concept
## 3 Digital Twins for Solar Photovoltaics
### Current Industry Trends  
Table 5 provides insight into industry-level commercialized software offering different DT solutions within the solar photovoltaics industry. Note that while the functionalities are highly varied, the list below is not exhaustive and is only to provide insight into DT activities for PV integration. \begin{table}
\begin{tabular}{p{56.9 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Ref(s)** & **ML Algorithm** & **Application** & **Input Data/Parameters** & **Output Data/Parameters** \\ \hline
[40] & CNN & Predicting I-V quality parameters using solar cell images & Electroluminescence and infrared images, reflectance measurements & \(V_{cc}\), \(I_{cc}\), \(FF\), \(\eta\), \(pFF\), \(FF_{0}\), \(suns\)\(V_{cc}\), \(pFF-FF\), \(FF_{0}-pFF\), \(pJ_{01}\), \(J_{02}\), \(I_{rec,1}\), \(I_{rec,2}\), \(R_{grid,fr}\), \(R_{grid,fr}\), \(R_{s}\), \(R_{p}\) \\
[50] & GA and ANN & Optimizing the configuration layout of a solar-thermal system to balance demand with production and operational efficiency & Field mirrors data: radius, height, material density, heat capacity, number, period required, irradiance & Optimal mirror positioning/angle \\
[64] & RF, DNN, Catboost & Compared 3 ML algorithms in a DT to predict performance & Weather data and historical energy data & Voltage and current \\
[111] & RF/LSTM/XGBoost/ ANN/CNN/LS-SVM & Compared various AI models using web-based PV-DT & Weighted data/PV energy yield data & Energy yield prediction \\
[112] & LUBE prediction + WSOA system & Short-term load forecasting in PV-DT grid system & Historical demand and energy data & Load demand and energy supply forecast \\
[53; 113] & GA & Very short-term prediction of PV-DT yield & Nominal power, tilt, azimuth, temperature irradiance, albedo, DC/AC & Now-cast prediction for PV yield \\
[114] & MMI + CRRBM + MTLA & Compared performance of a new combined ML approach with PV-DT to other algorithms for short-time forecasting & Historical demand and energy data & Short-time PV energy and load prediction \\
[54] & HYTREM + RF & Solar irradiance forecasting & Historical meteorological and irradiance data & Solar irradiance data forecast \\ \hline \hline \end{tabular}
\end{table} 
Table 4: ML algorithms in PV-DT. Figure 8: Functional architecture of statistical and AI-based solar power forecasting systems [6]. In addition to the above, there is a recent influx of companies looking into smart energy use in residential buildings, such as Bosch (Energy Manager), Eon (Home Energy Management), Empora (Emporia Vue Home Energy Monitor), and more. These companies provide data-driven energy management with some level of visualization. They often refer to their products as DT since they provide feedback on energy control. However, the products are severely limited in their ability to predict energy yield by the PV component, which becomes difficult due to the lack of standard WEC sensors. Furthermore, the well-established energy companies listed in Table 5 have been able to achieve a significant level of DT system management through the incorporation of different platforms. However, they still suffer significant limitations, particularly in terms of autonomous devices (robots). They are yet to implement a fully automated workflow concerning these new technologies. While many companies may be able to offer more than what is currently offered, there are a few critical challenges limiting the scope of functionalities, and a few of the challenges are discussed in Section 4.