## 5 Conclusions  
This study proposes a shadow movement prediction technique to improve the performance of MPPT controllers in PV systems used in large-scale solar farms. The proposed approach optimizes the MPPT algorithm by predicting the shadow movement pattern in a solar farm, thereby improving the MPPT speed.  
The proposed approach does not use any additional sensors and instead uses feedback from the status of existing MPPT controllers in the farm. After the detection of a shade, which is based on a change in the output of the solar panels, the shadow movement is determined after a specified number of time steps. Subsequently, the central controller employs this pattern to predict the shade's movement and gives the information to the MPPT controllers; both panels that will fall under the shade and those that will emerge from it predict MPP based on shadow movement prediction. This approach enhances the MPPT efficiency. The proposed approach uses an image-processing-based motion detection technique using normalized two-dimensional correlation coefficients for shadow movement detection in a solar farm.  
A distinctive feature of the proposed approach is its ability to differentiate between the movements of multiple shadows and identify each shadow's location and movement pattern individually. Thus, the approach enhances the performance of MPPT controllers  
Figure 25: (**a**) Area under the curve of the output power of the proposed (line in red) and conventional (line in blue) PV systems under decreased irradiation, and (**b**) the difference between the two systems (area in black).  
by accurately predicting shadow movement, optimizing the system's output power in two scenarios: entering or exiting shadow conditions. It increases the system's optimal output power and reduces the pressure on the control set, both in the DC bus and grid connection. The implementation results in a 15-hectare solar farm demonstrated that in shadow conditions with dynamic movement patterns, the output power of the solar panels improved, and the DC distortion in the farm's output was reduced. The simulation results showed that the proposed approach reduces the pressure on the control circuits by more than 70% in the studied 150,000 m\({}^{2}\) solar farm under shaded conditions.  
One benefit of the suggested approach is that it uses data from existing MPPT controllers and does not require extra sensors. However, further research is necessary for future studies to accurately assess the efficacy of the proposed approach, particularly considering the potential errors in predicting the MPP due to uncertainties and abrupt changes in cloud movement, as cloud movement patterns and shadow occurrence are completely unpredictable. Future work could also include predicting dust accumulation in windy conditions and adjusting the MPP accordingly. As another practical suggestion, it is important to consider the economic feasibility of implementing this system in a solar farm. In this context, the costs of establishing communication between each solar cell and the central system for output transmission, predicting cloud movement, and how the central system impacts controllers to reach maximum power quickly are crucial. Additionally, security measures to prevent communication disruption between the cells and the central system must also be considered.  
Conceptualization and supervision: J.M.R.S.T.; investigation, data collection, formal analysis, and writing--original draft preparation: A.A.G., V.H., N.S. and M.M.; writing--review and editing: J.J.M.M. and J.M.R.S.T. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available upon request from the corresponding author due to privacy restrictions.  
The authors declare no conflicts of interest.