## 5 Future Directions and Research Challenges  
This study has discussed the significance of the digitalization implementation in BIPV for achieving a resilient infrastructure. Initially, we presented an overview of the BIPV system, and later on, the discussed digital technologies that are suitable for digitalization. A high temperature of BIPV system, fault identification in BIPV system, and dust accumulation of BIPV system are areas where the significance of digitalization is identified and the proposed architecture is relevant. However, few parameters can be improved for the better implementation of digitalization in the BIPV system. Below, the discussion and recommendations are provided to enhance the few parameters, and they are as follows:  
_Next Generation Computing for Digitalization of the Photovoltaic Integration_  
(a) **Future directions**  
According to the findings of the above analysis, digitalization integration in the BIPV system is minimal. The adoption of digitalization has previously been proven in a variety of applications, where it facilitated the realization of robust infrastructure with a sustainable system. IoT, AI, ML, DL, edge computing, fog computing, UAV, and robotics technologies allow for real-time monitoring and control with intelligent analytics on the digital network [63]. Recently, the use of AR, VR, digital twins, and metaverse has increased in popularity in order to establish a virtual environment in which to visualize and feel everything in a realistic manner. * **ML and DL for image enhancement for flaws detection in real-time image data**  
In the scenario of the PV panels, the UAV with the assistance of an IR thermography camera captures the images of the PV panels during the aerial inspection. This image consists of noise and is unclear due to different environmental and technical parameters. From the previous studies, it has been concluded that by using adaptive histogram equalization grayscale and histogram equalization, the thermal solar panel images can be enhanced [64]. Moreover, with the different models such as artificial neural network (ANN) and convolutional neural network (CNN), the support vector machine achieved the highest accuracy in identifying the hotspot of faults in the enhanced images [64; 65; 66]. The above facts will encourage the implementation of the same models in the field of BIPV. * **Edge computing to implement DL for Intelligent BIPV data analytics**  
DL has already proven to be an efficient computation approach that has achieved high accuracy on computer vision data. DL demands high computation and memory. The inference is computationally expensive due to the very large dimensionality of high-resolution image data and the millions of computations that must be performed on the input data. The implementation of these computational tasks on a cloud server is challenging in terms of scalability, privacy, and bandwidth efficiency. Edge computing is capable of meeting the computational and memory requirements of DL with low latency [67]. Edge computing, which involves deploying a tiny mesh of compute nodes close to end devices, is a possible solution to address the high processing and low latency needs of deep learning on edge devices while also offering additional improvements in terms of bandwidth efficiency, privacy, and scalability [68]. Edge computing enables the loading of the AI and ML techniques in the edge device to perform real-time analytics regarding the BIPV system [69]. * **Fog computing for 6G assisted IoT network in BIPV** Generally, the implementation of BIPV on a scalable building or in a scalable community comprises a massive number of IoT-connected devices. However, based on current trends, the future BIPV system will be highly interconnected, encompassing a large number of devices, progressively digitized, and data-driven.