# Probabilistic Energy Forecasting using Quantile Regressions based on a new Nearest Neighbors Quantile Filter

## TL;DR

This paper introduces a novel nearest neighbors quantile filter method for probabilistic energy forecasting that simplifies the creation of quantile regressions, enabling fast and accurate predictions with less computational power.

## Contribution

The paper proposes a new nearest neighbors quantile filter technique that decouples quantile regression from complex data mining models, facilitating efficient probabilistic energy forecasts.

## Key findings

- Achieves similar accuracy to the 2014 Global Energy Forecasting Competition winner.
- Requires significantly less computational power for probabilistic forecasting.
- Demonstrates effectiveness on real-world energy dataset.

## Abstract

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.