SOLETE, a 15-month long holistic dataset including: Meteorology, co-located wind and solar PV power from

Area of Science:

• Renewable Energy Systems
• Meteorological Forecasting
• Power Systems Engineering

Background:

• Hybrid power plants combining wind and solar energy are increasingly relevant.
• Accurate forecasting is crucial for the integration of variable renewable energy sources.
• The SYSLAB laboratory in Denmark provides a controlled environment for energy research.

Purpose of the Study:

• To introduce the SOLETE dataset, a comprehensive resource for meteorological and power generation data.
• To facilitate research in meteorological, solar, and wind power forecasting.
• To support the development of hybrid power plant and distributed energy resource models.

Main Methods:

• Data collected from a meteorological station, an 11 kW wind turbine, and a 10 kW PV array.
• Measurements include environmental parameters and power output, recorded at 1 Hz and averaged over 5-minute and hourly intervals.
• Dataset spans 15 months (June 2018 - September 2019) and includes accompanying Python code for data analysis and model building.

Main Results:

• The SOLETE dataset provides synchronized time-series data on key meteorological variables (temperature, humidity, pressure, wind speed/direction, irradiance) and energy generation (wind turbine and PV inverter active power).
• Includes data averaged over 5-minute and hourly intervals, suitable for various forecasting timescales.
• Accompanied by Python scripts for data import, physics-informed machine learning model development for solar PV forecasting, and utility functions.

Conclusions:

• The SOLETE dataset is a valuable, publicly available resource for researchers in renewable energy forecasting.
• The provided code examples demonstrate practical applications for building forecasting models.
• Facilitates advancements in hybrid renewable energy system management and grid integration.