GANs have been considered the most efficient algorithm to capture the intermittency and fluctuation characteristics of wind and solar energy generation in recent years 11, 12.
Solar and wind generation data from on-site sources are beneficial for the development of data-driven forecasting models. In this paper, an open dataset consisting of data collected from on-site renewable energy stations, including six wind farms and eight solar stations in China, is provided.
Wind power generation data are in the wind_farms folder, which includes six Microsoft Excel files. The real-time power generation and weather conditions are recorded in these files. The basic information about each wind farm is listed in Table 1.
Similarly, in the solar dataset, total solar irradiance has the highest PCC with the power output, as shown in Fig. 6. Pearson correlation coefficient of different variables of the wind farms. WS_ x (i.e., wind speed at different heights) has the highest PCC with respect to power.
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Professional mobile solar container solutions with 20-200kWp solar arrays for mining, construction and off-grid applications.
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