Solar PV Positioning Optimizer Module (SPPOM with Giza Actions)

Team/Project Name: SPPOM with Giza Actions

Team Members:

• John William Q. Embate

The Problem     

     Transition towards renewable energy sources, especially solar photovoltaic systems, is critical in dealing with global warming and dependence on fossil fuels. Solar PV systems provide a sustainable alternative by capturing solar irradiance to generate electricity (Lane, 2022). Despite their potential, the efficiency of the solar panels is highly contingent on the positioning and orientation of solar panels relative to the sun, which varies significantly with geographic location and time of year (Masson et al., 2014). Optimal alignment is thus highly relevant in energy generation, but determining the ideal positioning is difficult, considering that various environmental factors influence the optimal positioning. Worsening this challenge is that solutions have to be established to function efficiently, individually for every household configuration, and in the broader regional context. This is also compounded by further sophisticated algorithms or technologies required to optimize solar panel placement, which in turn raises the need for innovative approaches toward making sure that decisions based on the data remain valid and secure.

The Solution   

    The Solar PV Positioning Optimizer Module (SPPOM) presents a complete solution that utilizes real-time IoT data collection, cloud analytics, and Zero-Knowledge Machine Learning (ZKML) within GizaTech's Orion Framework for the smart optimization of solar panel orientation. Using sensors to measure real-time data on the environmental factors such as intensity of sunlight, temperature, and angle of the panel, the system will orient the solar panel to capture most of the incoming energy. The data-driven approach is further enhanced by the application of machine learning models, which analyze patterns and predict optimal orientations. ZKML technology ensures the verifiability and security of these computations, allowing for the cryptographic proof of model inferences without exposing sensitive data. Apart from adapting to the specific needs of each household, this solution will also provide valuable insights for solar energy companies, allowing more effective planning and customer engagement strategies.

Value Proposition   

     One significant leap in solar energy optimization lies within the SPPOM, as it moves past the drawbacks that characterize conventional solar tracking. It works with cost-effectiveness and data-based options to optimize the tracker use. This approach focuses on positioning the panels optimally before installation, hence eliminating the costs associated with such systems while increasing the efficiency of energy production. This aspect is therefore very crucial for households and businesses going for solar energy investment, as they will definitely have a complete analysis of the potential energy yield based on accurate panel placement. Also, the use of ZKML technology in verifying data analysis and model prediction gives a good level of assurance and reliability for stakeholders in making informed decisions based on good and verified information. With SPPOM, it is possible to aggregate all this data to be in a position to conduct a serious market research in aid of identifying optimal localities to sit solar installation across, and as part of community planning.

The Technology Used   

     SPPOM integrates various technological features and tools in the implementation of its innovative solution. The setup is hardcore with hardware featuring a number of sensors, like accelerometers, humidity, and temperature sensors, that inform the orientation of the solar panels through real-time environment data. That data helps in training the ML models by showing the optimal positioning of the solar panels. The ML models' conversion to Cairo for ZKML processing makes integration with GizaTech's transpiler technology facilitate efficient deployment and scalability. The use of Cairo and the Orion Framework confers a solid guise to the data analytics process through which, in a transparent and verifiable manner, the solar panel positioning can be optimized. This technological ecosystem does not only facilitate optimal solar energy production but also carries with it the values of security and transparency in deploying AI and ML solutions in the renewable energy sector.

The Hardware Design   

The Schematic Diagram   

The CAD Model   

Future Recommendations   

• Integration of Advanced AI and Deep Learning Models: With the help of deep learning, use this to boost predictive accuracy in determining optimal positions for solar panels through analysis of more complex datasets, ranging from historical weather patterns to solar irradiance data across different seasons and geographical locations.
• Sensor Capabilities Expansion: Provide more functionality in the form of additional sensors, for instance, LIDAR or thermal imagers, for better environmental evaluation and potential obstacles (such as tree shadows, nearby buildings) affecting solar panel efficiency.
• User-Centric Design and Customization: Design an interface that's user-friendly and has customization options for inputs that work with specific options or limitations, such as aesthetic or spatial considerations, made by the owner of the house.

Giza Actions: 

-Grid Search Action Run For HyperParameter Tuning in PyTorch

Transpilation Process: 

Scarb build:

Deployment:

Verification:

Github Link: https://github.com/John-Embate/SPPOM-With-Giza-Actions