Solar Power Prediction System

AI-Powered Solar Energy Forecasting

Developed time series models to predict solar power generation for energy storage demonstration sites using PyTorch and Airflow.

Overview

Built a comprehensive solar power prediction system for Tainan FamilyMart Pingfeng store energy storage demonstration site. The system combines weather data, historical generation patterns, and machine learning models to provide accurate solar power forecasts.

Key Features

Real-time solar power prediction

Weather data integration

Automated data pipeline

Interactive dashboard

Performance monitoring

Challenges

Weather dependency in predictions

Seasonal variation handling

Real-time data processing

Model accuracy optimization

Solutions

Integrated multiple weather data sources

Implemented seasonal adjustment algorithms

Built robust data pipeline with Airflow

Applied ensemble modeling techniques

Results & Impact

Achieved 92% accuracy in daily predictions

Reduced energy storage costs by 25%

Successfully deployed to demonstration site

Enhanced AI showcase for the facility

Project Info

Role

Senior Engineer - ML Developer

Company

HD Renewables Co., Ltd.

Timeline

2023-04 - 2024-12

1 year 8 months

Technologies

PythonPyTorchAirflowDjangoMySQLTime Series AnalysisMLOps