Intra-Hour Photovoltaic Generation Forecasting Based on Multi-Source Data and Deep Learning Methods

Global issues pertaining to climate change have necessitated the rapid deployment of new energy sources, such as photovoltaic (PV) generation. In smart grids, accurate forecasting is essential to ensure the reliability and economy of the power system. However, PV generation is severely affected by meteorological factors, which hinders accurate forecasting. Various types of data, such as local measurement data, numerical weather prediction, and satellite images, can reflect meteorological dynamics over different time scales. This paper proposes a novel data-driven forecasting framework based on deep learning, which integrates an advanced U-net and an encoder-decoder architecture to cooperatively process multi-source (time series recording and satellite image) data.
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DAFT-E: Feature-Based Multivariate and Multi-Step-Ahead Wind Power Forecasting

At the recent 26th United Nations Climate Change Conference last year, more than 140 countries pledged to achieve net-zero emissions to combat climate change. And in a dramatic appeal to attain sustainability in the skies, Europe’s Flightpath 2050 initiated a bold effort to reduce CO2 emissions worldwide by 75%, NOx emissions by 90%, and the noise footprint by 60% by the midcentury mark.
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