Solar Energy Ecosystem Solutions Application - Krypton Solar

Problem Statement

Many individuals and businesses are interested in transitioning to solar energy but face challenges in determining the best solar panel installations, placements, and energy systems for their unique needs. Our application addresses this issue by leveraging data from solar APIs and employing machine learning to recommend optimal solar solutions.

A Brief of the Prototype

UML Diagram

Prototype Frontend

Prototype Description

Our solar energy ecosystem solutions application offers a comprehensive set of features to assist users in seamlessly integrating solar energy into their homes and businesses. Here's an overview of the key components:

• Data Collection and Integration:

  • Collects user location data, including latitude and longitude or the user's address.
  • Integrates with Solar APIs to obtain real-time solar radiation, weather, and historical solar energy generation data.

• Machine Learning Model:

  • Utilizes a machine learning model trained on historical solar data, including solar radiation patterns, weather conditions, and energy generation statistics from the user's region.

• Data Preprocessing:

  • Cleans and formats data for machine learning analysis.
  • Performs feature engineering to extract relevant information such as average daily solar radiation and historical energy generation trends.

• Solar Panel Recommendation:

  • Recommends the type and capacity of solar panels suitable for the user's location.
  • Considers factors like panel efficiency, tilt angles, and shading analysis.

• Placement Recommendations:

  • Suggests the best locations and orientations for solar panel installation based on factors like roof layout, available space, and shading.

• Budget Considerations:

  • Takes into account the user's budget constraints and recommends a solar energy system configuration that maximizes energy generation within the specified budget.

• User Benefits:

  • Optimized Solar System: Users receive personalized recommendations for efficient solar energy systems.
  • Ease of Decision-Making: Detailed information simplifies the decision-making process.
  • Increased Energy Efficiency: Maximum sunlight capture leads to energy efficiency and cost savings.
  • Environmental Impact: Minimized carbon footprint contributes to a greener environment.
  • Financial Savings: Users can expect reduced electricity bills and a return on investment.

Tech Stack

• Frontend: React
• Backend: Node.js
• Machine Learning: scikit-learn, TensorFlow
• Data Integration: Solar APIs
• Database: PostgreSQL
• Visualization: d3.js
• CSS Framework: Custom CSS

Step-by-Step Code Execution Instructions

To test the prototype and understand its functionalities, follow these steps:

1. git clone
2. Add your API Key to SolarService.js(in services) and index.html (in public)
3. Navigate to the Cloned folder and run yarn install and yarn start in the terminal
4. Then Run the app.html file in the browser to view the webapp.(all other files are interlinked)

Future Scope

Our application has significant future scalability and potential for expansion. Some potential enhancements include:

• Mobile application development for accessibility on various devices.
• Integration with additional renewable energy sources like wind power.
• Enhanced real-time energy consumption monitoring and forecasting.
• Further optimization for utility companies and solar farm operators with advanced solar asset management features.
• Collaboration with energy providers to facilitate the seamless transition to solar energy for users.

We aim to continually improve and scale our application to meet the evolving needs of users and the renewable energy industry.

Demo Video

Watch the demo