High-Level Project Summary
In this project, we have developed a robust system consisting of modern technologies in such cases as Backend & Frontend to accommodate with the climatological data acquired from NASA’s dataset to provide relevant suggestive data regarding the fixture of Solar Panels, which accommodates the challenge of providing customers or users access to relational or suggestive data regarding the climate, and efficiency. The ultimate importance of this implementation or project would be the ability to projectile the averages, and based on the averages, it also helps to make accurate decisions on the best location that a Solar Panel could be installed for the best Solar coverage across the globe.
Link to Project "Demo"
Link to Final Project
Detailed Project Description
Our project is helping to find better locations to set up the solar panels as well as to configure them to meet the perfect solar exposure. The application is allowing users to choose specific locations where they are interested to set up their solar panels or it will accept the user’s current location. The selected place will contain details about the sky conditions, also the solar Irradiance for the equator which is presented in graphs, where users can choose different time periods. For ease of use, the averages of the values are calculated with a formula to grade the location on the solar panel compatibility allowing the user to get a general idea. Moreover, we decided to include the data that assists to set panel angles to exact direction to better capture the energy. The 3D model is used to pinpoint the location of the sun so that users can easily understand the preferred angle for their solar panels. We are helping people to install the panels on their own, knowing and analyzing the locations that have better conditions for solar panels by providing essential information for both, general users and power users. As a result, our app will assist the public to use renewable and sustainable energy with less hassle.
In order to build the application we utilized Angular in combination with Ionic and Capacitor allowing us to use one codebase for many platforms such as web, iOS, or Android, saving the cost and time for development. We also use our own backend system using Python Flask deployed as a serverless function to AWS Lambda. In order to make deployment simpler Zappa, AWS CLI wrapper was used. For designing, we used Figma, a cross-platform UI prototyping tool. In order to collaborate remotely, Microsoft Teams and Discord were utilized. Lastly, our code versioning system is Git and the code repository is hosted on Github.
The application is complete and production ready with few limitations including caching, data analysis performance and potential low security exploits. The application was tested on iOS and Android, Google Chrome, Firefox.
Space Agency Data
Following space agency data were used:
SG_SAA - Azimuth Angle,
SG_SZA- Zenith Angle,
SI_EF_TILTED_SURFACE - Solar irradiance
CLOUD_AMT - Cloud Amount
CLRSKY_DAYS - Clear Sky Days
Specifically our backend system was making requests to available apis to process, analyze and clear data to provide information requested by the user in a presentable manner. We used azimuth and zenith angle monthly averages to present users with and to calculate the optimal angles for solar panels. a 3D model was used to place the sun in accordance with data received allowing the user to conveniently understand the best possible angle.
Solar irradiance, cloud amount and clear sky days averages were used to assess location on the solar panel placement, allowing users to determine the best location for solar panels. At the same time, power users are able to view data with granular control over a time period.
Hackathon Journey
We would describe the Space Apps Challenge as moderately intensive along with several challenging points towards architecting a solution to address the common issue faced by the community. Throughout this challenge, we have learned all the terminologies or addresses that are related to the climate, such as climatology, solar irradiance, sky amount, and more, which also relates to the metrics that come along. Our Team was well inspired and motivated with the whole idea of the hackathon where an approach towards challenges could be curated in accordance to the common issue, which can be scientifically related with a mixture of IT. Hence, leading to a robust solution towards gathering and developing a viable solution. Moreover, our approach towards developing a solution for the challenge has been more towards the logical end and with several elements of thoughts put into place into the scientific ecosystem or environment, thus leading to a well-architectured solution to ingest the needs / wants and demands of the users to provide accurate and clearer suggestions towards their ability to know the metrics of the climate from P2P (Point to Point) in order to effectively install their Solar Panel. Throughout this project, we have faced several challenges and setbacks, however, these setbacks or challenges have become our stepping stone towards achieving the next milestone in the project. With this, we have become more resilient towards providing or researching in regards to the issue or obstacles that we face so that we can progress accordingly to complete the project. Lastly, we would like to thank ourselves (All of us in the Team) for all our efforts and sleepless nights to complete the project and for the inner rush to learn and grow progressively.
References
Tools:
Amazon Lambda Functions,
Native Geocoding API
Chart js
Threejs
Ionic
Capacitor
Device Orientation API
Flask
Zappa
Data:
SG_SAA - Azimuth Angle,
SG_SZA- Zenith Angle,
SI_EF_TILTED_SURFACE - Solar irradiance
CLOUD_AMT - Cloud Amount
CLRSKY_DAYS - Clear Sky Days
Mapbox Geocoding API
Literature:
Tags
#apu, #asiapacificuniversity, #solar, #sun, #mobile, #energy, #renewable, #sustainable
Global Judging
This project has been submitted for consideration during the Judging process.