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.
@article{arxiv.1911.11536,
title = {Electricity Load Forecasting -- An Evaluation of Simple 1D-CNN Network Structures},
author = {Christian Lang and Florian Steinborn and Oliver Steffens and Elmar W. Lang},
journal= {arXiv preprint arXiv:1911.11536},
year = {2019}
}