A DATASET AT A GLANCE...

Solar power is one of the chief renewable energy sources for electricity generation, which can perhaps ensure net zero greenhouse gas emissions by 2050. In the past few years, the Solar PV (Photovoltaic) technology has an intensive usage in almost all the industrial and household appliances. Additionally, the solar energy is of utmost importance to plant growth in agriculture and food processing industries. Hence, solar radiation is very much necessary for the normal functioning of the aforesaid applications. Now, what would result, if the changing climatic conditions make the solar radiation unavailable for just a second? Only huge losses in terms of cost, production and machine efficiency!!! Hence, solar radiation prediction becomes vital to plan and operate the solar-based applications without massive losses. Researchers, seeking for a dataset to contribute towards this aspect, can make use of the “Solar Radiation Prediction” dataset from the Kaggle repository (https://www.kaggle.com/datasets/dronio/SolarEnergy). This dataset allows the researchers to develop approaches, which can precisely predict the solar radiation levels from wind speed, wind direction, temperature, humidity and pressure. These measurements, taken for about 4 months, is believed to provide an accurate solar radiation level prediction for the next day or so. Thanks to NASA for releasing the dataset, since numerous solar-based applications get benefitted of it. Image courtesy: www.freepik.com