High-Level Project Summary
Sunshine is a one stop mobile application for homeowners to monitor the solar power in their area, calculate output and purchase solar panels. We have introduced features, such as, Data Display, Graph Display, Solar Output Calculator and Solar Power Recommendation. Our app demonstrate its self financial sustainability, as we have spaces for our sponsors/ partners to advertise their solar-related product. We believe this is a win-win situation for us to ensure app development. We realised that homeowners often are not aware of the benefits of solar power and solar data related to their location, hence, we introduced features to reach our ultimate goal, popularisation of solar power globally.
Link to Project "Demo"
Link to Final Project
Detailed Project Description
Source code from Github:
https://github.com/wesleychiang96/sunshine
We have developed our app through flutter and backend through firebase. With a flutter framework which is fully customizable and available on all platforms (IOS, Android and Windows phone), we believe it is the best choice for us.
First, our historical data tracker has a built-in map function with a responsive API, which is much more accurate and up-to-date compared to traditional data-feeding systems. For our front end development, we have referred to multiple weather forecast application layouts and brainstormed in order to design the most user-friendly interface using Figma and implemented in flutter. For instance, we use minimal words but mostly icons and pictures so that every homeowner can understand its usage.
Secondly, with our novel ‘Output Calculator’ feature, this will have a long-lasting effect to motivate homeowners to use solar power by letting them to compare their current electricity output and the saved electricity. By inputting the local rates, they can even have a grasp of how much of the bill they are saving through solar power.
Lastly, we aim to provide a one-stop solution for users, hence, we introduce our Solar Panel Recommendation feature. Connected with local e-commerce platforms, users can use our feature to compare across platforms. Moreover, this will boost the local solar power industries as demand is up. Thus, it will produce a ripple effect as more investors are willing to invest in solar power and solar power is popularised.
Additionally, we have equipped our app with Google Map plugins to give our users the most accurate possible locating services.
We have referred to the world most successful application design and improvise on our application design to give users the most convenient experience.
We are able to save the user's data in our database, or they are able to log in through Facebook and Google to sync across their devices.
We have developed a setting page for users to manage their application preference.
This Graph data display will be linked to machine learning algorithm in the future to produce prediction of data for the users
We have also included a machine learning code written with Python Sci-Kit learn. Although we can’t integrate it in our app to produce a continuous solar irradiance prediction due to insufficient data from the weather forecast, it is a milestone of what we plan to do in the future. We believe what stands out from this is that our application is not only showing our users the history data, but also displaying future predictions for them to consider. Furthermore, we have built an output calculator from scratch. It seems like a simple algorithm, but the effect it can achieve is more far-reaching. It connects to our main goal which is the popularisation of solar power, as from our research, most people are not using solar power because they failed to realise how significant it is in saving household energy.
Application Requirements
- Any Operating System (ie. MacOS X, Linux, Windows)
- Any IDE with Flutter SDK installed (ie. IntelliJ, Android Studio, VSCode etc)
- A little knowledge of Dart and Flutter
We have tested our application on Android phones with android 11 and it is available to download! Check our final project link!
Space Agency Data
We have used NASA Power API as it contains the most comprehensive data and responsive API. We managed to send a http request to the API which will in turn feed our mobile application with data. It inspires us as we can get a huge chunk of solar-related information from NASA without having to go through a complicated process. And, it is the generosity of the space agencies that make our project possible by providing reliable and accurate data.
We also plan to get reliable data from National Weather Service (NWS) space agency in the future for weather forecasting using machine learning. As we believe integrating the power of Artificial Intelligence will be a huge improvement for our current application.
Data used:
Main Sources of Dataset:
NASA POWER Web Portal
https://power.larc.nasa.gov/
Monthly historical Dataset
https://power.larc.nasa.gov/api/temporal/monthly/point?parameters=QV2M,WS2M,TS,CLOUD_AMT&community=RE&longitude=111.8305&latitude=2.2873&format=JSON&start=2019&end=2020
Parameters obtained:
earth skin temperature, specific humidity at 2 meters, wind speed at 2 meters, cloud amount
Hackathon Journey
The Space Apps journey was very fruitful, as we are able to put our idea into practise. Our team members come from Borneo Island, where the usage and benefits of solar energy are often neglected. Hence, it drives us to choose this challenge, as we believe our efforts are able to change the situation, and perhaps drive our local economy. We had multiple setbacks and barriers to overcome; however, with our teamwork, we are able to resolve them case by case. The road to success is strewn with setbacks, so we are not giving up easily as this is for the goods of our earth. We would like to thank the organising committee, Realfun learning centre and our mentor, Mr Teo. Without them and their helpful workshops, we would not be able to discover more about our idea, let alone building an application from scratch. Thanks to our seniors, we are able to solve programming challenges as the applications are built with flutter, a considerable new language. Make Cents out of Solar!
References
Data used: Sources of Dataset:
NASA POWER Web Portal
https://power.larc.nasa.gov/
Monthly historical Dataset
https://power.larc.nasa.gov/api/temporal/monthly/point?parameters=QV2M,WS2M,TS,CLOUD_AMT&community=RE&longitude=111.8305&latitude=2.2873&format=JSON&start=2019&end=2020
Parameters obtained:
earth skin temperature, specific humidity at 2 meters, wind speed at 2 meters, cloud amount
Tools used:
Flutter and Dart
Canva
Figma
Python
Powtoon
IDE(VS Code, Android Studio)
Tags
#solar #machinelearning #AI #Artificialintelligence #flutter # dart #calculator #sunlight #nasa #spaceappchallenge #ios #android #windows
Global Judging
This project has been submitted for consideration during the Judging process.