Payra Wind Data

Citation Author(s):
Md. Ashaduzzaman
Niloy
Submitted by:
Md. Ashaduzzama...
Last updated:
Thu, 03/27/2025 - 00:35
DOI:
10.21227/enzm-qj50
Data Format:
*.csv
License:
Creative Commons Attribution
76 Views
Categories:
Machine Learning
Power and Energy
Electric Utility
Sensors
Keywords:
wind power, wind farm, Bangladesh
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Abstract 

This dataset contains high-resolution wind measurement data collected from 22 channels at varying heights, providing valuable insights for wind energy assessment, atmospheric research, and meteorological studies. The dataset includes wind speed, wind direction, and environmental parameters measured at multiple altitudes ranging from 10m to 120m. Each channel records parameters such as average wind speed, standard deviation, minimum and maximum values, gust speed, and wind vane direction. Additionally, atmospheric parameters such as temperature, relative humidity, and pressure are included. The data is stored in CSV format, enabling easy access and analysis for researchers and engineers. This dataset can support studies related to wind resource assessment, turbulence analysis, and renewable energy forecasting.

Instructions: 

Dataset Instructions: Wind Data (15 October 2018 – 14 October 2019)

1. Overview

This dataset contains wind measurement data collected from 15 October 2018 to 14 October 2019 at multiple heights, ranging from 10m to 120m. The data includes wind speed, wind direction, and atmospheric parameters, which are crucial for wind energy assessment, meteorological studies, and atmospheric research.

2. File Format

  • The dataset is provided as a comma-delimited CSV file (.csv).

  • Each row represents a timestamped observation.

  • Column headers describe the measurement type, height, and units.

3. Column Descriptions

  1. ChX_Anem_HH.m_S_Avg_m/sAverage wind speed at HH.m heightm/s
  2. ChX_Anem_HH.m_S_SD_m/sStandard deviation of wind speed at HH.m heightm/s
  3. ChX_Anem_HH.m_S_Min_m/sMinimum recorded wind speed at HH.m heightm/s
  4. ChX_Anem_HH.m_S_Max_m/sMaximum recorded wind speed at HH.m heightm/s
  5. ChX_Anem_HH.m_S_Gust_m/sWind gust speed at HH.m heightm/s
  6. ChX_Vane_HH.m_Dir_Avg_degAverage wind direction at HH.m heightDegrees
  7. ChX_Vane_HH.m_Dir_SD_degStandard deviation of wind direction at HH.m heightDegrees
  8. ChX_Vane_HH.m_Dir_GustDir_degGust wind direction at HH.m heightDegrees
  9. ChX_Analog_HH.m_Avg_%RHAverage relative humidity%RH
  10. ChX_Analog_HH.m_SD_%RHStandard deviation of relative humidity%RH
  11. ChX_Analog_HH.m_Min_%RHMinimum relative humidity%RH
  12. ChX_Analog_HH.m_Max_%RHMaximum relative humidity%RH
  13. ChX_Analog_HH.m_Avg_mbAverage atmospheric pressuremb
  14. ChX_Analog_HH.m_SD_mbStandard deviation of pressuremb
  15. ChX_Analog_HH.m_Min_mbMinimum atmospheric pressuremb
  16. ChX_Analog_HH.m_Max_mbMaximum atmospheric pressuremb
  17. ChX_Analog_HH.m_Avg_CAverage temperature°C
  18. ChX_Analog_HH.m_SD_CStandard deviation of temperature°C
  19. ChX_Analog_HH.m_Min_CMinimum temperature°C
  20. ChX_Analog_HH.m_Max_CMaximum temperature°C

4. Data Usage

  • Time Series Analysis: The dataset can be used for temporal analysis of wind patterns.

  • Wind Resource Assessment: Ideal for evaluating wind energy potential at different heights.

  • Meteorological Research: Can support climate studies and atmospheric modeling.

  • Turbulence Analysis: Useful for analyzing turbulence intensity and wind gust patterns.

5. 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.

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Submitted by: Md. Ashaduzzaman Niloy

 

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