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

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

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Metadaten
Author:Christian Lang, Florian Steinborn, Oliver SteffensORCiDGND, Elmar Wolfgang LangORCiD
URN:urn:nbn:de:bvb:898-opus4-16649
URL / DOI:https://arxiv.org/abs/1911.11536
URL / DOI:http://itise.ugr.es/ITISE2019_vol2.pdf
ISBN:978-84-17970-78-9
Parent Title (English):International Conference on Time Series and Forecasting (ITISE 2019), Proceedings of Papers Vol. 2, 25-27 September 2019, Granada (Spain)
Document Type:conference proceeding (article)
Language:English
Year of first Publication:2019
Publishing Institution:Ostbayerische Technische Hochschule Regensburg
Release Date:2021/05/17
Tag:CNN; STLF; con-volutional networks; energy load forecasting; neural networks
First Page:797
Last Page:806
Institutes:Fakultät Angewandte Natur- und Kulturwissenschaften
Fakultät Angewandte Natur- und Kulturwissenschaften / Labor Bauphysik
Begutachtungsstatus:peer-reviewed
research focus:Energie und Mobilität