Daily Solar energy predictor machine learning Python Django

1. Daily Solar Energy Generation Prediction
Group Members:
Aftab Haider (cu-505-2014)
Mudasir Rehman (cu-652-2016)
Supervisor: SIR ABID HAIDER (AP EED)

2. Contents
Introduction
Advance Literature survey
Objectives
Design
Software Requirement Specifications
Programming initiative
Tested Product
Future Extensions
References

3. Introduction
• To develop a tool for predicting the solar energy generation
prediction.
• for the residential and commercial purpose and solar
power plant as well,
• Will predict the generation of solar energy
• That how much solar energy will be generated per day by
the solar panels
• or by the whole solar power plant .
• We will built this tool
• Using weather forecasting
• and the previous solar energy data.

4. Introduction
We have built a tool which will predict solar energy
• This will predict the solar energy generation from
the weather forecasting.
Online

5. • Predicting Solar Energy Production with Machine Learning [1]
Advance Literature survey
• New solar forecasting tool could help increase efficiency
and reduce energy costs [2]
• Solar Forecasting: Methods, Challenges, and Performance
[3]
• A Two-Step Approach to Solar Power Generation
Prediction Based on Weather Data Using
Machine Learning [4]
• Solar Power Forecasting with Machine Learning Techniques [5]

6. • My goal with this project is to build a tool that makes information
regarding one’s potential for switching to solar available to a
wider audience, bringing down the cost Energy and improve
efficiency and saving of Energy.
• I accomplished this goal by Building a machine learning model
that predicts the daily energy production of a prospective solar
installation so that the user can efficiently use the energy.
Objectives

7. Design (Desktop View)

8. Design (Mobile View)

9. Design (Code)

10. Design (Code)

11. Software requirement specifications
• Functional Requirement
• Historical solar data
• Area
• Solar system specifications
• Weather forecasting
• Final result

12. Software requirement specifications
• Non Functional Requirement
• Availibilty
• Reliability
• Accurancy in results
• Easy to use

13. Programming Intiative
• Python
• Django,Html,Css (For Web Application )

14. • Scikit learn (for machine learning in python)
• Excel (for database system)
• python weather API (for weather forecasting)

15. Tested product

16. Model used
Linear model
• We have used linear regression model in our
project
• Features
Kilowatt Size/Capacity
Azimuth
Tilt
Technology type
Diffuse Horizontal Irradiance (DHI)
Direct Normal Irradiance (DNI)
Wind Speed

17. Future Extensions
Installation Cost
• In future we are adding a feature of installation cost
which will predict the cost of installing solar
system
Annual Return
• In future we are adding a feature of annual returns
which will predict the annual savings.

18. https://medium.com/@evanbaker/predicting-solar-energy-
production-with-machine-learning-19fcab295e58
[1]
References
https://phys.org/news/2017-08-solar-tool-efficiency-energy.html
[2]
https://www.researchgate.net/publication/283240263_Solar_Forecasti
ng_Methods_Challenges_and_Performance
[3]
https://www.mdpi.com/2071-1050/11/5/1501/pdf
[4]
http://www.diva-portal.org/smash/get/diva2:1215661/FULLTEXT02
[5]

19. Thank You