momentarek1/Europe_Cities_PVGIS_Solar

Europe Cities PVGIS Solar Data

A structured dataset containing solar radiation and photovoltaic-related data for European cities, generated using the Photovoltaic Geographical Information System (PVGIS).

The dataset is designed for solar energy analysis, photovoltaic performance estimation, data science, machine learning, and renewable-energy research.

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🌍 Dataset Overview

This dataset contains PVGIS-based solar and photovoltaic information collected for a set of cities across Europe.

The dataset was generated using a fixed photovoltaic panel configuration characterized by:

• Slope / Inclination: 45°
• Azimuth / Orientation: 180°
• Geographical scope: European cities
• Data source: PVGIS — Photovoltaic Geographical Information System
• Primary domain: Solar Energy / Photovoltaics
• Format: Tabular data

PVGIS is an official European Commission / Joint Research Centre system that provides solar-radiation and photovoltaic-performance information for locations around the world.

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☀️ What is PVGIS?

PVGIS (Photovoltaic Geographical Information System) is a solar-energy information system developed by the European Commission's Joint Research Centre (JRC).

It provides information about:

• Solar radiation
• Solar irradiation
• Photovoltaic electricity production
• Temperature
• PV system performance
• Hourly, daily, and monthly solar data
• Different panel orientations and inclinations
• Fixed and tracking PV configurations

PVGIS also provides APIs that allow researchers and developers to automatically retrieve solar and PV data for many geographical locations.

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🎯 Dataset Purpose

The main purpose of this dataset is to provide a ready-to-use collection of PVGIS information for European cities.

It can be used to investigate questions such as:

How does solar energy potential vary between European cities?

How does geographic location affect photovoltaic production?

Which cities have higher solar irradiation?

Can machine learning predict PV energy production from geographic and meteorological features?

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📐 PV System Configuration

The dataset name indicates the following configuration:

Slope   = 45°
Azimuth = 180°

Slope

The slope represents the inclination of the photovoltaic panel relative to the horizontal plane.

Horizontal surface
       │
       └── 0°
       
PV panel
       ╲
        ╲ 45°
         ╲

A slope of 45° means that the PV plane is inclined 45 degrees from the horizontal.

PVGIS defines slope as the angle of the PV modules from the horizontal plane for a fixed, non-tracking installation.

Azimuth

Azimuth represents the orientation of the photovoltaic panel.

PVGIS documents its azimuth convention relative to due South:

        North
          │
          │
West ─────┼───── East
          │
        South

In the PVGIS convention:

0°     = South
+90°   = West
-90°   = East

Therefore, when interpreting a dataset containing azimuth_180, the exact generation convention should be verified from the original data-generation script/API parameters rather than assuming that 180° has the same meaning across all solar-data systems.

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🗺️ Geographic Coverage

The dataset focuses on cities across Europe.

Each city can be associated with geographic information such as:

• City name
• Latitude
• Longitude
• Elevation
• Country
• Solar-resource measurements
• PV performance indicators

The geographical coordinates are important because PVGIS calculations are location-dependent.

PVGIS accepts locations using latitude and longitude and uses geographical and meteorological data to estimate solar radiation and PV performance.

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📊 Data Categories

Depending on the PVGIS endpoint and configuration used to generate the dataset, the data may contain variables related to:

Geographic Features

City
Country
Latitude
Longitude
Elevation

Solar Radiation

Global Irradiation
Direct Irradiation
Diffuse Irradiation
In-plane Irradiation

Meteorological Variables

Air Temperature
Wind Speed
Solar Elevation

Photovoltaic Performance

PV Energy Production
Daily PV Production
Monthly PV Production
Yearly PV Production
PV System Losses

PVGIS hourly radiation outputs can include global in-plane irradiance, direct irradiance, diffuse irradiance, reflected irradiance, sun height, air temperature, and wind speed.

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📅 Temporal Resolution

PVGIS can provide solar and photovoltaic information at several temporal resolutions:

Hourly
Daily
Monthly
Yearly / Aggregated

For example, monthly PV calculations can provide average daily production, monthly production, irradiation on the plane of the array, and variation statistics.

The exact temporal resolution in this dataset depends on the specific PVGIS endpoint used during data collection.

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📁 Suggested Dataset Structure

A typical project structure can look like:

europe-cities-pvgis/
│
├── data/
│   ├── raw/
│   │   └── europe_cities_pvgis_data_slope_45_azimuth_180.csv
│   │
│   └── processed/
│       └── europe_cities_pvgis_processed.csv
│
├── notebooks/
│   ├── data_exploration.ipynb
│   ├── visualization.ipynb
│   └── analysis.ipynb
│
├── src/
│   ├── data_collection.py
│   ├── preprocessing.py
│   └── analysis.py
│
├── README.md
└── requirements.txt

---

🔄 Data Generation Pipeline

The dataset can be generated using the following workflow:

European Cities
       │
       ▼
Latitude / Longitude
       │
       ▼
PVGIS API
       │
       ▼
Solar & Meteorological Data
       │
       ▼
Fixed PV Configuration
       │
       ├── Slope = 45°
       └── Azimuth = 180°
       │
       ▼
Data Collection
       │
       ▼
Data Cleaning
       │
       ▼
CSV / Tabular Dataset

PVGIS provides non-interactive APIs specifically for automated calculations and data retrieval.

---

🧹 Data Preprocessing

Before using the dataset for machine learning or statistical analysis, several preprocessing steps may be useful.

1. Missing Values

Check for:

NaN
Null
Empty values
Invalid coordinates

2. Duplicate Records

Remove duplicate city/date combinations when necessary.

3. Geographic Validation

Validate:

Latitude
Longitude
Elevation

4. Unit Consistency

Make sure all measurements use consistent units.

For example:

Irradiance → W/m²

Energy → kWh

Temperature → °C

Wind Speed → m/s

Angles → degrees

PVGIS documentation specifies the units of its different radiation and PV variables in its output definitions.

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📈 Possible Exploratory Data Analysis

The dataset can be analyzed using:

Geographic Analysis

Compare solar potential between:

Northern Europe
Central Europe
Southern Europe
Eastern Europe
Western Europe

City-Level Analysis

Rank cities according to:

Solar irradiation
PV production
Average temperature
Annual energy yield

Seasonal Analysis

Analyze differences between:

Winter
Spring
Summer
Autumn

Correlation Analysis

Investigate relationships between:

Latitude
Longitude
Elevation
Temperature
Solar irradiation
PV production

---

🤖 Machine Learning Applications

The dataset can be used for several machine-learning tasks.

1. PV Energy Prediction

Predict photovoltaic energy production from:

Latitude
Longitude
Elevation
Temperature
Solar irradiation
Month
Day / Hour

Example:

Geographic + Weather Features
             │
             ▼
       ML Regression
             │
             ▼
    Predicted PV Energy

Possible algorithms:

• Linear Regression
• Random Forest
• XGBoost
• LightGBM
• Gradient Boosting
• Support Vector Regression
• Neural Networks

---

2. Solar Potential Classification

Cities can be categorized into groups such as:

Low Solar Potential
Medium Solar Potential
High Solar Potential

This can be formulated as a classification problem.

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3. City Ranking

A scoring system can be developed to rank European cities based on their photovoltaic potential.

For example:

PV Potential Score
        │
        ├── Solar Irradiation
        ├── PV Production
        ├── Temperature
        └── Seasonal Stability

---

📊 Visualization Ideas

Useful visualizations include:

Solar Potential Map

Display cities geographically and color them according to annual solar irradiation.

PV Production Ranking

City A  █████████████
City B  ███████████
City C  █████████
City D  ███████

Monthly Solar Production

Plot:

Month → PV Energy

to visualize seasonal variation.

Correlation Matrix

Analyze relationships between:

Latitude
Longitude
Temperature
Irradiation
PV Output

---

🧪 Example Python Workflow

import pandas as pd

df = pd.read_csv(
    "europe_cities_pvgis_data_slope_45_azimuth_180.csv"
)

print(df.head())
print(df.info())
print(df.describe())

Basic missing-value analysis:

missing = df.isnull().sum()

print(missing)

Basic city-level analysis:

city_summary = (
    df.groupby("city")
      .mean(numeric_only=True)
)

print(city_summary)

---

🔌 PVGIS API

The dataset can be reproduced or extended using the official PVGIS API.

PVGIS provides APIs for automated access to solar radiation and photovoltaic calculations. The API supports parameters such as:

latitude
longitude
PV peak power
PV technology
system losses
slope
azimuth

For fixed PV systems, the API defines:

angle  → inclination angle
aspect → orientation / azimuth

and supports automated CSV or JSON output.

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🧮 PV Performance

PVGIS can estimate the energy produced by a grid-connected PV system while considering factors such as:

• Solar radiation
• Temperature
• Wind speed
• PV module technology
• Module orientation
• Module inclination
• System losses

The grid-connected PV tool provides monthly and yearly estimates of PV energy production and irradiation on the PV plane.

---

🌱 Potential Applications

This dataset can support projects involving:

• Renewable energy
• Solar energy forecasting
• Photovoltaic optimization
• Smart cities
• Sustainable urban planning
• Energy-demand planning
• Green AI
• Climate-aware machine learning
• Energy analytics
• Geographic data science
• GIS-based solar analysis

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⚠️ Important Considerations

Fixed Panel Configuration

The dataset uses a fixed panel configuration rather than dynamically optimizing the panel orientation for every city.

Therefore, results should be interpreted specifically under the selected:

Slope = 45°
Azimuth = 180°

configuration.

PVGIS itself can calculate optimum inclination and, when requested, optimum inclination and orientation for fixed systems.

Geographic Effects

PV production depends strongly on location.

Factors include:

Latitude
Longitude
Elevation
Solar climate
Temperature
Local horizon

PVGIS can also account for terrain-based horizon effects using elevation data, although very nearby objects such as buildings and trees are not represented by the calculated horizon.

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📚 Data Source

The primary data source is:

PVGIS — Photovoltaic Geographical Information System

Developed and maintained by the:

European Commission — Joint Research Centre (JRC)

Official PVGIS:

PVGIS Official Website

PVGIS provides free access to solar-radiation and photovoltaic-performance information and supports both interactive tools and APIs.

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📖 References

1. European Commission — Joint Research Centre. Photovoltaic Geographical Information System (PVGIS).

2. PVGIS 5 User Manual — Solar radiation, PV systems, slope and azimuth definitions.

3. PVGIS Grid-Connected PV Documentation — PV energy production and system parameters.

4. PVGIS API Documentation — Automated solar and PV data retrieval.

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🔑 Keywords

PVGIS
Solar Energy
Photovoltaics
PV
Renewable Energy
Europe
European Cities
Solar Radiation
Solar Irradiation
PV Energy
Solar Dataset
Machine Learning
Data Science
Energy Analytics
GIS
Geospatial Data
Sustainable Energy
Green AI
Climate Data
Renewable Energy Forecasting

---

🚀 Project Goal

The overall goal of the dataset is to provide a structured foundation for analyzing solar-energy and photovoltaic potential across European cities under a standardized fixed-panel configuration.

European Cities
       ↓
Geographic Data
       ↓
PVGIS Solar Data
       ↓
45° Slope
       ↓
180° Azimuth
       ↓
Solar / PV Features
       ↓
Analysis & Visualization
       ↓
Machine Learning
       ↓
Solar Energy Insights

This makes the dataset suitable as a foundation for renewable-energy analytics, photovoltaic forecasting, geographic analysis, and machine-learning research.