2021 Tabular Data https://fordatis.fraunhofer.de/handle/fordatis/215
http://dx.doi.org/10.24406/fordatis/142

Energy Consumption Curves of 499 Customers from Spain

Mey, Oliver (Fraunhofer-Institut für Integrierte Schaltungen IIS, Institutsteil Entwicklung Adaptiver Systeme EAS); Schneider, André (Fraunhofer-Institut für Integrierte Schaltungen IIS, Institutsteil Entwicklung Adaptiver Systeme EAS); Enge-Rosenblatt, Olaf (Fraunhofer-Institut für Integrierte Schaltungen IIS, Institutsteil Entwicklung Adaptiver Systeme EAS); Yesnier, Bravo  ; Stenzel, Pit  

IIS Fraunhofer-Institut für Integrierte Schaltungen - Institutsteil Entwicklung Adaptiver Systeme EAS
Files in This Item:
FileDescriptionSizeFormat 
20201015_consumption.xlsxenergy consumption curves of the 499 customers (time series, hourly resolution)21,06 MBMicrosoft Excel XMLDownload/Open
20201015_profiles.xlsxprofiles of the 499 customers19,21 kBMicrosoft Excel XMLDownload/Open
20201015_weather.xlsxweather data (outside temperature) of the customer's regions (time series, hourly resolution)18 MBMicrosoft Excel XMLDownload/Open

Abstract

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.

Technical Information

The dataset contains the measured energy consumption of 499 customers in Spain. The file 20201015_consumption.xlsx contains time series with an hourly resolution for each of the customers. The energy consumption is measured in kWh. In addition to energy consumption weather data is available. The file 20201015_weather.xlsx contains the outside temperature for the region of each customer as time series with an hourly resolution. The file 20201015_profiles.xlsx contains the meta data for the 499 customers. Each customer is assigned to one of the 68 customer profiles (such as private households, shops, bakeries).
Classification
600 Technik, Medizin, angewandte Wissenschaften
Keywords
energy consumption
hourly consumption
different customer profiles
private households
Funder
Bundesministerium für Verkehr und digitale Infrastruktur BMVI (Deutschland)
Fraunhofer Group
Energietechnologien und Klimaschutz
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1 fordatis/215 2021-08-06 13:42:36.0
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