  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.
