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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Powerlines and Wildfires: Overview, Perspectives, and Climate Change: Could There Be More Electricity Blackouts in the Future?
Overhead powerlines cross extensive areas of forest and grasslands, and these areas are often flammable and can burn. Wildfire is a natural phenomenon important to many ecosystems around the globe, but also capable of considerable damage to people and communities. As a result of human activity in natural spaces, people have altered wildfire regimes over time, and wildfires have become a threat to people, to their property, and infrastructure.
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