Deep neural networks for the quantile estimation of regional renewable energy production

Abstract

Wind and solar energy forecasting have become crucial for the inclusion of renewable energy in electrical power systems. Although most works have focused on point prediction, it is currently becoming important to also estimate the forecast uncertainty. With regard to forecasting methods, deep neural networks have shown good performance in many fields. However, the use of these networks for comparative studies of probabilistic forecasts of renewable energies, especially for regional forecasts, has not yet received much attention. The aim of this article is to study the performance of deep networks for estimating multiple conditional quantiles on regional renewable electricity production and compare them with widely used quantile regression methods such as the linear, support vector quantile regression, gradient boosting quantile regression, natural gradient boosting and quantile regression forest methods. A grid of numerical weather prediction variables covers the region of interest. These variables act as the predictors of the regional model. In addition to quantiles, prediction intervals are also constructed, and the models are evaluated using different metrics. These prediction intervals are further improved through an adapted conformalized quantile regression methodology. Overall, the results show that deep networks are the best performing method for both solar and wind energy regions, producing narrow prediction intervals with good coverage.

Photovoltaic Power Prediction Based on Hybrid Deep Learning Networks and Meteorological Data

“…The point prediction method is a deterministic approach, but it fails to capture the probability distribution and fluctuation range of the prediction results. In complex weather conditions, photovoltaic power generation exhibits significant fluctuations within short periods, thereby compromising the accuracy of the point prediction method and posing challenges for maintaining a stable and secure power grid [ 18 , 19 , 20 ]. Probabilistic density prediction, on the other hand, offers a more comprehensive forecasting technique by effectively representing uncertainty as a probability distribution centered around the predicted value.…”