Probabilistic Energy Forecasting using Quantile Regressions based on a new Nearest Neighbors Quantile Filter
This work addresses the need for efficient probabilistic forecasting in energy systems, particularly for online services with limited computing resources, though it is incremental as it builds on existing quantile regression techniques.
The paper tackles the challenge of creating probabilistic energy forecasts using quantile regressions without a non-differentiable cost function, by introducing a nearest neighbors quantile filter. The method achieves similar accuracy and speed as the winner of the 2014 Global Energy Forecasting Competition but requires much less computational power.
Parametric quantile regressions are a useful tool for creating probabilistic energy forecasts. Nonetheless, since classical quantile regressions are trained using a non-differentiable cost function, their creation using complex data mining techniques (e.g., artificial neural networks) may be complicated. This article presents a method that uses a new nearest neighbors quantile filter to obtain quantile regressions independently of the utilized data mining technique and without the non-differentiable cost function. Thereafter, a validation of the presented method using the dataset of the Global Energy Forecasting Competition of 2014 is undertaken. The results show that the presented method is able to solve the competition's task with a similar accuracy and in a similar time as the competition's winner, but requiring a much less powerful computer. This property may be relevant in an online forecasting service for which the fast computation of probabilistic forecasts using not so powerful machines is required.