Day-ahead regional solar power forecasting with hierarchical temporal convolutional neural networks using historical power generation and weather data

arXiv:2403.01653v1 Announce Type: new
Abstract: Regional solar power forecasting, which involves predicting the total power generation from all rooftop photovoltaic systems in a region holds significant importance for various stakeholders in the energy sector. However, the vast amount of solar power generation and weather time series from geographically dispersed locations that need to be considered in the forecasting process makes accurate regional forecasting challenging. Therefore, previous work has limited the focus to either forecasting a single time series (i.e., aggregated …

abstract arxiv convolutional neural networks cs.lg data energy energy sector forecasting hierarchical importance networks neural networks power regional sector solar solar power solar power forecasting stakeholders systems temporal total type vast weather weather data