@inproceedings{LangSteinbornSteffensetal., author = {Lang, Christian and Steinborn, Florian and Steffens, Oliver and Lang, Elmar Wolfgang}, title = {Electricity Load Forecasting - An Evaluation of Simple 1D-CNN Network Structures}, series = {International Conference on Time Series and Forecasting (ITISE 2019), Proceedings of Papers Vol. 2, 25-27 September 2019, Granada (Spain)}, booktitle = {International Conference on Time Series and Forecasting (ITISE 2019), Proceedings of Papers Vol. 2, 25-27 September 2019, Granada (Spain)}, isbn = {978-84-17970-78-9}, url = {http://nbn-resolving.de/urn:nbn:de:bvb:898-opus4-16649}, pages = {797 -- 806}, abstract = {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}, language = {en} }