# Harnessing the Cloud: A Novel Approach to Smart Solar Plant Monitoring
## 6 Discussion  
This study aims to evaluate the effectiveness (in terms of cost, efficiency, and reliability) of a novel, cloud-based architecture for monitoring solar power plants by leveraging the Internet of Things (IoT), edge computing, and cutting-edge technologies, e.g., cloud computing and artificial intelligence (AI). We proposed that the low-cost design approach successfully balances the trade-offs between cost and performance using minimum hardware resources, e.g., sensors and compute boards, and present the best technological practices that will serve the needs of both small-scale and large-scale solar PV plants. The fog/edge layer preprocess the data before sending them to cloud platforms like AWS, reducing latency and bandwidth usage. We proposed and established evidence, which includes the fact that the integration of the edge layer is pivotal in managing the intermittent nature of solar energy and their sensitivity to environmental changes accurately in real time and with the least overhead and cost. The proposed system integrates cloud, edge, and AI technologies, which not only meet operational needs for handling the massive data generated by the monitoring system but also ensure affordability and accessibility, making it viable for wider adoption in the solar industry.  
We have presented a literature review on the existing architectures (centralized and de-centralized), hardware resources, and communication technologies. These architectures have shortcomings, including (1) the use of proprietary software, (2) being expensive or not easily available hardware, (3) limitations in communication technologies, and (4) dependency on a specific technology chosen for implementation. As a result, it makes them less generalizable, less cost-effective, and prone to failures to be applied in other platforms or large-scale industrial systems. Moreover, previous studies highlight the potential of cloud computing in solar plant monitoring systems but miss the context of synergy between edge computing and cloud services. This potential gap is filled by proposing an edge layer that preprocesses the data before sending them to the cloud and then implementing AI-based analytics directly within the cloud architecture. Our focus in this article includes (1) the cost and simplification aspect of the architecture, (2) detailed monitoring,and (3) system insights, which can be achieved by increasing the number of sensed parameters but that will incur some additional cost and complexity. Also, there could be a potential risk of data loss or over-simplification during preprocessing at the edge layer, omitting some valuable insights.  
Future explorations in the context of this research can be performed on the economic analysis of scaling the system across different geographical and climate conditions. Moreover, the evaluation of durability and maintenance requirements of the devices at the perception and edge layer can be explored in future. Further investigation can be carried out into the integration of other renewable energy sources. Cloud computing and AI are already dominating the modern technological world and new tools and technologies are frequently evolving. Exploring further cloud computing platforms/technologies and AI models that can leverage such IoT systems can be a rich avenue for further research.