# Electricity Load Forecasting -- An Evaluation of Simple 1D-CNN Network Structures

arXiv: https://arxiv.org/abs/1911.11536 · HTML: https://mlsanity.com/paper/1911.11536/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Nov 2019.

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

- [Artificial Intelligence and Design of Experiments for Assessing Security of Electricity Supply: A Review and Strategic Outlook](https://mlsanity.com/paper/2112.04889/) (2021)

## Abstract (arXiv)

This paper presents a convolutional neural network (CNN) which can be used for forecasting electricity load profiles 36 hours into the future. In contrast to well established CNN architectures, the input data is one-dimensional. A parameter scanning of network parameters is conducted in order to gain information about the influence of the kernel size, number of filters, and dense size. The results show that a good forecast quality can already be achieved with basic CNN architectures.The method works not only for smooth sum loads of many hundred consumers, but also for the load of apartment buildings.