Predictions of energy consumption are crucial for energy retailers to minimize deviations from energy acquired in the day-ahead market and the actual consumption of their customers. The increasing spread of smartmeters means that retailers have access to hourly consumption values of all their contracted customers in realtime. Using machine learning algorithms, these hourly values can be used to calculate predictions for the future energy consumption of the customers. The present data set allows the training and validation of AI-based prediction models.
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The primary data of the SaGA corpus are made up of 25 dialogs of interlocutors (50), who engage in a spatial communication task combining direction-giving and sight description. Six of those dialogues with data only from the direction giver are available including audio (.wav) and video (.mp4) data. The secondary data consists of annotations (*.eaf) of gestures and speech-gesture referents, which have been completely and systematically annotated based on an annotation grid (cf. the SaGA documentation). The corpus is comprised of of 9881 isolated words and 1764 isolated gestures. The stimulus is a model of a town presented in a Virtual Reality (VR) environment. Upon finishing a "bus ride" through the VR town along five landmarks, a router explained the route as well as the wayside landmarks to an unknown and naive follower. The SaGA Corpus was curated for CLARIN as part of the Curation Project "Editing and Integration of Multimodal Resources in CLARIN-D" by the CLARIN-D Working Group 6
This dataset contains vibration data recorded on a rotating drive train. This drive train consists of an electronically commutated DC motor and a shaft driven by it, which passes through a roller bearing. With the help of a 3D-printed holder, unbalances with different weights and different radii were attached to the shaft. Besides the strength of the unbalances, the rotation speed of the motor was also varied. This dataset can be used to develop and test algorithms for the automatic detection of unbalances on drive trains. Datasets for 4 differently sized unbalances and for the unbalance-free case were recorded. The vibration data was recorded at a sampling rate of 4096 values per second. Datasets for development (ID "D[0-4]") as well as for evaluation (ID "E[0-4]") are available for each unbalance strength. The rotation speed was varied between approx. 630 and 2330 RPM in the development datasets and between approx. 1060 and 1900 RPM in the evaluation datasets. For each measurement of