⚡ GridSense

GridSense is a modular backend system for real-time extraction, normalization and prediction of energy data in Spain.
The project is designed for energy forecasting, intelligent monitoring, and estimating environmental impact from real electricity demand.

Early version (Phase 1): Data extraction and CSV storage only.

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🌐 APIs Used

• Red Eléctrica de España (ESIOS API) – hourly electricity demand
  → https://www.esios.ree.es/es/api-documentation

• PVGIS API – solar irradiance and photovoltaic simulation data
  → https://re.jrc.ec.europa.eu/pvg_tools/en/

• OpenWeatherMap API – weather data → https://openweathermap.org/api

• Our World in Data (CO₂) – emission factors and global stats
  → https://ourworldindata.org/co2-and-other-greenhouse-gas-emissions

• Solcast (solar forecast) – solar radiation forecasts and historical estimates
  → https://toolkit.solcast.com/

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🚀 Getting Started

1. Clone the repository:

git clone https://github.com/your-username/GridSense.git
cd GridSense

2. Install dependencies:

pip install -r requirements.txt

3. Create a .env file with your API token:

ESIOS_TOKEN=your_ree_api_token

4. Run the extractor script:

python app/main.py

This will generate a CSV with the latest demand data in data/consumo_ree.csv.

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📦 Commit Convention

This project follows the Conventional Commits format:

():

Examples:

• feat(ree): extract and store hourly demand data from REE
• fix(config): correct token loading from .env
• docs(readme): add project introduction and usage guide

Valid types: feat, fix, docs, style, refactor, test, chore

Never commit sensitive files like .env. It is ignored by default.

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🧠 Future Plans

• Add data normalization and resampling by time and region
• Implement energy demand forecasting (Prophet, LSTM)
• Include environmental impact equivalence in CO₂
• Build an API layer with FastAPI to serve predictions
• Deploy a real-time dashboard

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Open-Meteo Dataset Documentation

This dataset contains hourly atmospheric and solar radiation data from the Open-Meteo historical API for multiple cities in Spain, covering the period 2005–2020.

File location

• Combined file: data/raw_data/openmeteo_all_cities.csv
• Per-city files: data/{city}/raw/openmeteo_{city}_2005_2020.csv

Columns and Units

Column	Description	Unit
time	Datetime in ISO 8601 format	YYYY-MM-DD HH:MM
temperature_2m	Air temperature at 2 m height	Celsius (°C)
cloudcover	Total cloud cover	%
windspeed_10m	Wind speed at 10 m height	m/s
winddirection_10m	Wind direction at 10 m height (0° = North)	Degrees (°)
shortwave_radiation	Global horizontal irradiance (GHI)	W/m²
direct_radiation	Direct normal irradiance (DNI)	W/m²
diffuse_radiation	Diffuse horizontal irradiance (DHI)	W/m²
cloud_cover	Alternate cloud cover value	%
city	Lowercase city name	Text

Notes

• All values are hourly averages.
• Timezone is automatically adjusted to the location.
• Data source: Open-Meteo Archive API

Example usage

import pandas as pd
openmeteo = pd.read_csv("data/raw_data/openmeteo_all_cities.csv", parse_dates=['time'])




© GridSense · MIT License