High-Level Project Summary
The main goal of the project is to enhance individuals experience in using solar panels in their homes by developing an application that gives them solar power data directly related to their exact location and to accomplish this, the deliverables for the application were set to be as follows:1_Provide extracted solar power data from Nasa resource.2_Provide extracted data from near-area (neighborhood) solar panel users.3_Predict future solar power levels in the same location.and the requirements are:1_API utilization for resource data.2_Simulation for neighborhood usage.3_AI model for prediction.
Link to Project "Demo"
Link to Final Project
Detailed Project Description
The project is a mobile application that help individuals to use solar panels in their homes by providing them data related to their exact location. The flow of using the application is as follows:
1_The user starts the application. 2_The application detects the location of the mobile. 3_The application sends API request to extract data from NASA resource for the same location. 4_The application displays power vs time chart from the extracted data.
5_The application uses the same data to predict future values 6_The application extracts data of the neighborhood solar panels from the application server assuming that all panels are registered to the application.
The current phase of the project:
1_Used Python to do API request and succeeded.
2_Designed the logo and UI.
3_Deveoped a prediction model using Python and applied linear regression as baseline neural network.
4_Used Cisco Packet Tracer with Python and MQTT for neighborhood and IoT simulation.
5_Developed a python code in local host to perform as a server for the IoT simulation.
Space Agency Data
this source was used: https://power.larc.nasa.gov/data-access-viewer/ for the data it has related to solar power irradiation
The following parameters were considered for chart graphing and predicting:
T2MDEW : Dew/Frost Point at 2 Meters
ALLSKY_SRF_ALB : All Sky Surface Albedo
ALLSKY_SFC_UVA : All Sky Surface UVA Irradiance
V10M : Northward Wind at 10 Meters
ALLSKY_SFC_UVB : All Sky Surface UVB Irradiance
ALLSKY_SFC_PAR_TOT : All Sky Surface PAR Total
ALLSKY_SFC_UV_INDEX : All Sky Surface UV Index
ALLSKY_SFC_LW_DWN : All Sky Surface Longwave Downward Irradiance
CLRSKY_SFC_LW_DWN : Clear Sky Surface Longwave Downward Irradiance
RH2M : Relative Humidity at 2 Meters
SZA : Solar Zenith Angle
QV2M : Specific Humidity at 2 Meters
WD10M : Wind Direction at 10 Meters
T2M : Temperature at 2 Meters
T2MWET : Wet Bulb Temperature at 2 Meters
PS : Surface Pressure
ALLSKY_KT : All Sky Insolation Clearness Index
WS10M : Wind Speed at 10 Meters
PRECTOTCORR : Precipitation Corrected
Hackathon Journey
It was an interesting experience were we were able to widen our knowledge in a very short time, and to increase our knowledge in other field such as space, physics and energy, as we were motivated to provide an interesting idea and strong project in less than two days.
We chose our challenge because we think it is a global issue and a local issue, moreover it is an interesting topic with a lot or researches and studies, and during our journey we tried to utilize our group and individual experiences as we come from different backgrounds related to business, electronics and computer science.
References
https://power.larc.nasa.gov/data-access-viewer/
https://pixabay.com/photos/suburbs-homes-neighbors-2211335/
Python
Cisco Packet Tracer
MQTT
linear regression as baseline neural network
Adobe Illustrator
Tags
#solar #iot
Global Judging
This project has been submitted for consideration during the Judging process.