Cargando…

Household electricity consumption in Greece: A dataset based on socio-economic features

The electricity consumption of a residence depends on many factors such as the habits and economical status of the occupants, the properties of the household and many more. To shed more light on the subject a data set for households was created. The data were collected in Greece through an anonymous...

Descripción completa

Detalles Bibliográficos
Autores principales: Mischos, Stavros, Gkalinikis, Nikolaos Virtsionis, Manolopoulou, Aikaterini, Dalagdi, Eleana, Zaikis, Dimitrios, Lazaridis, Aristotelis, Vlachava, Danai, Lagouvardos, Kostantinos, Vrakas, Dimitrios
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10293984/
https://www.ncbi.nlm.nih.gov/pubmed/37383765
http://dx.doi.org/10.1016/j.dib.2023.109232
Descripción
Sumario:The electricity consumption of a residence depends on many factors such as the habits and economical status of the occupants, the properties of the household and many more. To shed more light on the subject a data set for households was created. The data were collected in Greece through an anonymous survey that comprises 26 questions, resulting in 188 data points from 104 households from different time periods. Each data point contains attributes that are divided into four categories. In the first category, the information is about the household data such as the type and properties of the residence. Next, occupants’ socio-economic features are gathered. In this category information for the number and type of the occupants, the employment status and the total income of the residents is included. The third category of attributes is about the energy-related occupants’ behavior. Finally, the location of the household was provided from the users to estimate the weather conditions for the provided time. Data augmentation was performed to discover non-trivial relationships between the data points. Thus, a secondary set of features was computed based on the raw attributes and is also included. The provided data set can be used to extract insights that could be valuable during the imminent energy crisis.