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Solar energy forecasting with numerical weather predictions on a grid and convolutional networks

Accurate forecasting of solar energy is of vital importance for energy system integration and development of the energy Internet. In this paper, we propose a solar energy forecasting model with numerical weather predictions on grids and convolutional networks. Gaussian process regression is used to transform the incoming solar energy values of arbitrarily located solar farms to a regular grid so that we can leverage the learning ability of convolutional networks. The convolutional network takes the input of numerical weather predictions and produces solar energy values on the grid points, which are then used to calculate the forecasts for the solar farms. Experiments on the American Meteorological Society solar energy prediction dataset demonstrate the effectiveness of the proposed model. The proposed model has the potential to be generalized to different tasks related to renewable energy integration.