Time series weather dataset for predicting photovoltaic Energy in Portugal

Published: 13 May 2024| Version 1 | DOI: 10.17632/m26hk4ysvz.1
Mahmudul Islam,


Data were collected from the University of Évora's Verney weather station, Portugal. This dataset consists of comprehensive weather data collected from Verney weather station over four years. The dataset is stored in the primary "Verney" directory. Within this directory, there are subdirectories for each year ranging from 2020 to 2023. Each yearly folder is subdivided into further folders named "1", "10", "60", and "1440", representing the data recording intervals in minutes. Accordingly, the weather data is organized into intervals of one minute, ten minutes, one hour, and one day. The naming convention for the files reflects the recording interval and the date; for instance, one-minute interval files are named as "verney_YYYYMMDD_1.dat", such as “verney_20230101_1.dat”. The ten-minute files follow the format "verney_YYYYMMDD_10.dat", like “verney_20230101_10.dat”. Files for the sixty-minute interval are labeled "verney_YYYYMM_60.dat", e.g., “verney_202301_60.dat”, and the daily interval files are designated as "verney_YYYYMM_1440.dat", for example, "verney_202301_1440.dat". The file names clearly encode both the date and the data interval, using “YYYYMMDD” for minute and ten-minute intervals and “YYYYMM” for hourly and daily intervals, with suffixes (1, 10, 60, 1440) indicating the recording interval in minutes. This structured format facilitates the straightforward retrieval and analysis of data for research.


Steps to reproduce

Data were collected from the University of Évora's Verney weather station, Portugal. In the data collection process, we have used instruments such as a Thermohygrometer for ambient temperature and humidity readings, a Pyranometer belonging to Skye brand to get global solar radiation measurements, an Anemometer for wind speed measurements, a Rain gauge for getting readings of precipitation. The measurements include temperature, humidity, wind speed, solar radiation, precipitation, etc. Data were captured at 1, 10, 60, and 1440-minute intervals. Timestamped readings were automatically recorded which ensured a continuous and accurate time-series dataset. The data is in .dat format.


Universidade de Evora Catedra Energias Renovaveis


Machine Learning, Renewable Energy, Weather, Energy Forecasting


King Saud University