Solar Data Explorer

C 66 completed
Data Tool
cli / json · tiny
18
Files
8,228
LOC
0
Frameworks
4
Languages

Pipeline State

completed
Run ID
#352257
Phase
done
Progress
1%
Started
Finished
2026-04-13 01:31:02
LLM tokens
0

Pipeline Metadata

Stage
Cataloged
Decision
proceed
Novelty
66.68
Framework unique
Isolation
Last stage change
2026-05-10 03:35:28
Deduplication group #52709
Member of a group with 2 similar repo(s) — canonical #79973 view group →
Top concepts (2)
Project DescriptionData/ML
All rows above produced by Repobility · https://repobility.com

AI Prompt

Create a command-line ETL pipeline in Python that processes TMY3 solar weather data downloaded from Kaggle. The pipeline should follow a medallion architecture: first, download the raw data (Bronze); second, clean and enrich it by handling date/time issues and joining station metadata (Silver); and finally, compute the weekly average GHI and DNI per station, outputting the final result as a JSON file in the specified structure (Gold). The process should use `polars` for efficient data frame processing and map stations to their respective ISO/RTO regions.
python cli etl solar-energy json polars kaggle data-processing scripting
Generated by gemma4:latest

Catalog Information

An ETL pipeline that transforms TMY3 solar weather data into weekly averages and enriches it with energy market context.

Description

This project implements a data pipeline that ingests raw TMY3 solar weather datasets, cleans and normalizes the information, and aggregates it into weekly averages. It then enriches the resulting dataset with contemporaneous energy market prices, providing a comprehensive view of solar potential versus market demand. The pipeline is modular, allowing users to customize extraction, transformation, and loading stages, and outputs ready‑to‑use CSV files for downstream analytics. Targeted at researchers, analysts, and energy planners, it simplifies the preparation of high‑quality data for forecasting, policy analysis, and investment decisions. By automating repetitive data handling tasks, it reduces manual effort and minimizes errors in solar‑energy studies.

الوصف

يُقدِّم هذا المشروع خط أنابيب معالجة بيانات يهدف إلى تحويل مجموعات بيانات الطقس الشمسية TMY3 الخام إلى متوسطات أسبوعية دقيقة. يبدأ بمرحلة الاستخراج التي تُحمِّل البيانات الخام، ثم يُنظِّف ويُطوِّرها عبر عمليات تحويل تشمل التوحيد والتصحيح. تُضاف بعد ذلك معلومات أسعار سوق الطاقة المتزامنة، ما يتيح رؤية شاملة لتوافق الإمكانات الشمسية مع الطلب والسعر في السوق. تُنتج العملية ملفات CSV جاهزة للاستخدام في التحليلات المستقبلية أو نماذج التنبؤ. يستهدف الباحثين، محللي الطاقة، ومخططي المشاريع الشمسية الذين يحتاجون إلى بيانات موثوقة وسهلة التحضير. يقلل هذا النظام من الجهد اليدوي ويحد من الأخطاء المحتملة في معالجة البيانات، مما يسرّع اتخاذ القرارات الاستراتيجية في مجال الطاقة المتجددة.

Novelty

6/10

Tags

solar-data etl weather-analytics energy-market-integration time-series-processing data-cleaning forecasting-support

Claude Models

claude-opus-4.6

Quality Score

C
66.0/100
Structure
60
Code Quality
90
Documentation
75
Testing
0
Practices
68
Security
100
Dependencies
60

Strengths

  • Code linting configured (ruff (possible))
  • Consistent naming conventions (snake_case)
  • Good security practices \u2014 no major issues detected

Weaknesses

  • No LICENSE file \u2014 legal ambiguity for contributors
  • No tests found \u2014 high risk of regressions
  • No CI/CD configuration \u2014 manual testing and deployment

Recommendations

  • Add a test suite \u2014 start with critical path integration tests
  • Set up CI/CD (GitHub Actions recommended) to automate testing and deployment
  • Add a LICENSE file (MIT recommended for open source)

Security & Health

4.1h
Tech Debt (B)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (1)
Citation: Repobility (2026). State of AI-Generated Code. https://repobility.com/research/
Unknown
License
3.3%
Duplication
Full Security Report AI Fix Prompts SARIF SBOM

Languages

json
91.2%
python
6.6%
markdown
2.0%
toml
0.3%

Frameworks

None detected

Concepts (2)

Page rendered by Aljefra Mapper · scored by Repobility (https://repobility.com)
CategoryNameDescriptionConfidence
Repobility · MCP-ready · https://repobility.com
auto_descriptionProject DescriptionETL pipeline that processes TMY3 solar weather data (hourly observations from 1,000+ US stations) into weekly average irradiance per station, output as JSON.80%
auto_categoryData/MLdata-ml70%

Quality Timeline

1 quality score recorded.

View File Metrics

Embed Badge

Add to your README:

![Quality](https://repos.aljefra.com/badge/76367.svg)
Quality BadgeSecurity Badge
Export Quality CSVDownload SBOMExport Findings CSV