Abstract

This dataset contains high-resolution solar and wind measurement data collected from the Feni region, Bangladesh, spanning from 2017 to 2019. Logged at a 1-minute interval, the dataset provides a comprehensive record of atmospheric and meteorological conditions, essential for renewable energy analysis, climatological studies, and resource assessment.

The dataset includes wind data from multiple anemometers and wind vanes, along with solar radiation components such as Direct Normal Irradiance (DNI), Global Horizontal Irradiance (GHI), and Diffuse Horizontal Irradiance (DHI). Additionally, it records temperature, humidity, pressure, precipitation, and sun elevation parameters. Quality flags are included for each measured parameter to ensure data reliability.

Key Features of the Dataset:

Temporal Resolution: 1-minute data logging
Wind Parameters: Wind speed (average & max) from multiple anemometers, wind direction from vanes
Solar Parameters: DNI, GHI, DHI, and Global Tilted Irradiance (GTI)
Environmental Parameters: Temperature (air & reference cells), relative humidity, pressure, precipitation
Quality Control: Flags indicating data integrity

Potential Applications:

Wind and Solar Energy Assessment: Evaluation of hybrid renewable energy potential
Meteorological and Climate Studies: Analysis of atmospheric variations over time
Grid Integration & Forecasting: Data-driven insights for power grid stability

The dataset is provided as a comma-delimited CSV file (.csv), where each column represents a specific measurement tag. Users can access detailed data tag information within the file to interpret each parameter correctly. This dataset serves as a valuable resource for researchers, engineers, and policymakers focusing on Bangladesh’s renewable energy sector and atmospheric science.

Instructions:

Instructions for Using the Solar and Wind Dataset (Feni Region, 2017–2019)

This dataset provides 1-minute resolution measurements of solar and wind parameters collected from the Feni region, Bangladesh, between 2017 and 2019. It is stored as a comma-separated values (CSV) file, making it easy to process with Python, MATLAB, Excel, or any data analysis tool.

1. File Format

Format: Comma-delimited CSV (.csv)
Structure: Each row corresponds to a 1-minute timestamp, and each column represents a specific parameter
Time Format: The dataset may contain a timestamp column in DD-MM-YYYY HH:MM format

2. Data Parameters

The dataset includes multiple wind, solar, and meteorological parameters. Below are key groups:

Wind Data:

Wind Speed (m/s): Average & Maximum from multiple anemometers
Wind Direction (°): Measured from wind vanes

Solar Data:

DHI (Diffuse Horizontal Irradiance, W/m²)
DNI (Direct Normal Irradiance, W/m²)
GHI (Global Horizontal Irradiance, W/m²)
GTI (Global Tilted Irradiance, W/m²)

Environmental Data:

Temperature (°C): Air temperature & reference cell temperatures
Relative Humidity (%RH)
Atmospheric Pressure (hPa)
Precipitation (mm)

Quality Control Flags:

Each main parameter has an associated flag column, indicating data quality and potential anomalies.

3. How to Access the Data

Download the CSV file from IEEE Dataport.
Open the file using software such as Excel, Python (pandas), MATLAB, or R.
Use the first row for column headers and ensure the data is parsed as numeric values.

4. Data Processing and Analysis

Filtering Data: Use flags to filter out unreliable data
Time Series Analysis: Analyze patterns in wind speed and solar radiation over time
Renewable Energy Assessment: Use for potential solar-wind hybrid power feasibility studies