Solar PV Positioning Optimizer Module

A hardware module for creating data-driven reports during the solar pre-installment phase. Optimize positioning and orientation of Solar PVs. Leverage data for clustering solar privileged communities.

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  • Solar PV applications

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Description

Challenge 3: Finding the best locations for solar communities


The Problem   

     One of the most concerning issues of the twenty-first century is global warming and climate change. Solar energy production in cities is one way to reduce our reliance on fossil fuels and is a good strategy to mitigate global warming by lowering greenhouse gas emissions (Masson, Bonhomme, Salagnac, Briottet, & Lemonsu, 2014). Also, among the various renewable energy options, solar energy is gaining popularity due to the numerous benefits it can provide in terms of sustainable electricity generation. Solar energy is becoming a part of homes and businesses all over the world due to the unlimited supply it can offer. However, for maximum power output, it is heavily reliant on two factors: orientation and positioning. Aside from that, solar panel installations are also dependent on their geographic location around the world due to the fact that the sun is not always in a stationary position and constantly changes position relative to the earth over time. To get the maximum power that can be generated from solar panels, it needs to be pointed in the direction where it is most exposed to the sun. As a result, the pre-installation of solar PVs is critical in the utilization of solar energy (Novergy, 2019).


The Solution    

     The implementation of the Solar PV Positioning Optimizer Module (SPPOM) will include both hardware and software. In the hardware section, an IoT device with an accelerometer, temperature, humidity, real-time clock, and current sensors will be developed. It also has miniature solar panels and servos, which will serve as the primary orientation and positioning basis. The sensor's data can be stored locally using an SD card data logger as backup storage, or it can be pushed to a cloud storage platform. The collected data will be used in the software implementation to train machine learning models and conduct data analysis on how certain factors such as humidity, local weather, orientation, and so on may affect the total power output of Solar PVs on a specific household. The models will be used to optimize the orientation and position of Solar PVs in order to generate the most power.


Value Proposition   

     Even though solar trackers produce more electricity, they are usually not worth the additional investment. Installing more solar panels would be less expensive than including a tracking system because solar panels are now more affordable than ever (Lane, 2022). As a result, it is more worthwhile to optimize the positioning and orientation of solar panels, which our device and services can provide with the assistance of our team’s expert knowledge. The SPPOM accomplishes this task by collecting accurate data that can affect the efficiency of solar PVs for specific households before investing in one. Beyond providing statistical reports and developing machine learning models for selecting the best position and orientation on the installation of solar PVs on specific households, the data will become more valuable for concerned stakeholders, market research, and design teams for finding the best locations for solar communities by using unsupervised learning on the overall data to cluster privileged areas of solar energy against the economic well-being of a community, local weather, and etc. As a result, it can assist businesses in the solar industry in more effectively targeting potential customers.


The Technology Used    

     The hardware includes vital sensors such as an accelerometer, humidity sensor, temperature sensor, real-time clock, and current sensor. It also has actuators, such as servos, that move a miniature solar panel at different angles to collect power output. The collected data will be stored on an SD card data logger as a local backup storage device, as well as pushed to the cloud. Backup storage will be critical in the event of an internet outage or unexpected disconnection, particularly in remote areas where internet connectivity is difficult. The data will then be processed, analyzed, and used to train machine learning models to determine the best position and orientation for Solar PVs that produce the most power output.


The Hardware Design   


Front and Back View


Casing View


The Schematic Diagram


Schematic Diagram


The CAD Model

CAD Model


Conclusion and Results

On this file, we conducted a detailed and thorough analysis of the data acquired during one of our sample field tests:

https://github.com/John-Embate/SPPOM-BTF-2022/blob/main/Data%20Analysis%20and%20Modeling/Data%20Analysis%20and%20Modeling.ipynb


Github Repository

https://github.com/John-Embate/SPPOM-BTF-2022


The Team   

  • John William Embate
    • BS - Computer Engineering (2nd year)
  • Jay Randolph Comeros 
    • BS - Computer Engineering (3rd year)
  • Brent Parinasan
    • BS - Computer Engineering (3rd year)
  • Andrea Agbay 
    • BS - Information Technology (3rd year)
  • Cherry Anne Embate
    • BS - Civil Engineering (3rd year)

     Apollo Analytics is made up of members with different specializations and a diverse set of skills, which leads them to collaborate, connect ideas, and share perspectives in the process of building and developing the product. With the sense of great camaraderie and collaborative effort, the team is passionate about developing creative solutions upon building the future.


References:

(2019, March 28). Retrieved from Novergy: https://www.novergysolar.com/heres-why-orientation-and-positioning-of-solar-panels-is-so-important/

Lane, C. (2022, January 3). Retrieved from Solar Reviews: https://www.solarreviews.com/blog/are-solar-axis-trackers-worth-the-additional-investment

Masson, V., Bonhomme, M., Salagnac, J.-L., Briottet, X., & Lemonsu, A. (2014, June 4). Solar panels reduce both global warming and urban heat island. doi:https://doi.org/10.3389/fenvs.2014.00014

 


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