Bright Future: Solar Energy Forecasting Solution

Team Members

• Lê Minh Quý: Lead Engineering
• Phùng Đức Anh
• Nguyễn Viết Hoàng
• Bùi Thị Ngọc Ánh

Overview

The Bright Future project aims to address key environmental and operational challenges associated with solar energy adoption and production. Our solution is a data-driven approach to forecast solar energy output and provide actionable insights for households, businesses, and power plant operators.

Environmental and Industry Challenges

1. Environmental Impact of Electricity Production:

   • Conventional electricity generation contributes significantly to pollution and environmental degradation.

2. Hesitation in Solar Panel Installation:

   • Many households and businesses are uncertain about the benefits and long-term viability of investing in solar panels.

3. Maintenance Scheduling Difficulties for Power Plants:

   • Solar power plants often face challenges in efficiently planning maintenance due to variable energy production and unpredictable weather conditions.

Our Solution

The Bright Future platform leverages machine learning and advanced analytics to predict solar energy production, thereby addressing the unique needs of three primary user groups:

1. Households:

   • Gain accurate insights into the potential energy savings from solar panel installation.
   • Understand expected return on investment (ROI) and environmental impact.

2. Businesses:

   • Receive tailored recommendations for integrating solar energy into operations.
   • Optimize energy usage and reduce dependency on conventional power sources.

3. Power Plant Operators:

   • Predict energy production to enhance scheduling for maintenance and grid management.
   • Increase operational efficiency and minimize downtime.

Key Features

• Solar Energy Prediction: Accurate forecasts based on historical and real-time data.
• Personalized Recommendations: Custom insights based on location, consumption patterns, and financial goals.
• User-Friendly Interface: Intuitive dashboards for monitoring and decision-making.

Technology Stack

• Programming Languages: TypeScript, Python
• Frontend: React.js (hosted on Vercel)
• Backend: FastAPI
• Machine Learning Frameworks: TensorFlow, Scikit-learn
• Database: PostgreSQL
• Cloud Services: Vercel for frontend deployment, AWS/GCP for backend scalability
• Shell Scripts: Utility scripts for project setup and maintenance