# Neural Network Middle-Term Probabilistic Forecasting of Daily Power Consumption

arXiv: https://arxiv.org/abs/2006.16388 · HTML: https://mlsanity.com/paper/2006.16388/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2020.

## Built on by (most cited first, 1 of 1)

- [RNN(p) for Power Consumption Forecasting](https://mlsanity.com/paper/2209.01378/) (2022)

## Abstract (arXiv)

Middle-term horizon (months to a year) power consumption prediction is a main challenge in the energy sector, in particular when probabilistic forecasting is considered. We propose a new modelling approach that incorporates trend, seasonality and weather conditions, as explicative variables in a shallow Neural Network with an autoregressive feature. We obtain excellent results for density forecast on the one-year test set applying it to the daily power consumption in New England U.S.A.. The quality of the achieved power consumption probabilistic forecasting has been verified, on the one hand, comparing the results to other standard models for density forecasting and, on the other hand, considering measures that are frequently used in the energy sector as pinball loss and CI backtesting.