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. TheThis 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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| Author: | Christian Lang, Florian Steinborn, Oliver SteffensOTHORCiDGND, 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 |
| Research Center of Energy and Resources - RCER | |
| Research Center for Artificial Intelligence - RCAI | |
| Fakultät Angewandte Natur- und Kulturwissenschaften / Labor Bauphysik | |
| Begutachtungsstatus: | peer-reviewed |
| research focus: | Nachhaltige Lebensräume |
| Frontdoor-URL: | https://opus4.kobv.de/opus4-oth-regensburg/1664 |
