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Applying a 1D-CNN Network to Electricity Load Forecasting

  • 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 number of nodes. Furthermore, different dropout methods are applied to the CNN and are evaluated. The results show that a good forecast quality can already be achieved with basic CNN architectures, the dropout improves the forecast. The method works not only for smooth sum loads of many hundred consumers, but also for the load of single apartment buildings.

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Metadaten
Author:Christian Lang, Florian Steinborn, Oliver SteffensORCiDGND, Elmar Wolfgang LangORCiD
DOI:https://doi.org/10.1007/978-3-030-56219-9_14
Parent Title (English):Theory and Applications of Time Series Analysis
Publisher:Springer
Editor:O. Valenzuela, F. Rojas, L.J. Herrera, H. Pomares, I. Rojas
Document Type:Part of a Book
Language:English
Year of first Publication:2020
Release Date:2021/10/04
Tag:CNN; Convolutional networks; Energy load forecasting; Neural networks; STLF
First Page:205
Last Page:218
Konferenzangabe:International Conference on Time Series and Forecasting, ITISE 2019
Institutes:Fakultät Angewandte Natur- und Kulturwissenschaften
Fakultät Angewandte Natur- und Kulturwissenschaften / Labor Bauphysik
Begutachtungsstatus:peer-reviewed
research focus:Energie und Mobilität
Licence (German):Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG