ABSTRACT
The aim of the SOLETE dataset is to support researchers in the meteorological, solar and wind power forecasting fields. Particularly, co-located wind and solar installations have gained relevance due to the rise of hybrid power plants and systems. The dataset has been recorded in SYSLAB, a laboratory for distributed energy resources located in Denmark. A meteorological station, an 11 kW wind turbine and a 10 kW PV array have been used to record measurements, transferred to a central server. The dataset includes 15 months of measurements from the 1st June 2018 to 1st September 2019 covering: Timestamp, air temperature, relative humidity, pressure, wind speed, wind direction, global horizontal irradiance, plane of array irradiance, and active power recorded from both the wind turbine and the PV inverter. The data was recorded at 1 Hz sampling rate and averaged over 5 min and hourly intervals. In addition, there are three Python source code files accompanying the data file. RunMe.py is a code example for importing the data. MLForecasting.py is a self-contained example on how to use the data to build physics-informed machine learning models for solar PV power forecasting. Functions.py contains utility functions used by the other two.
