Forecasting power load curves from spatial and temporal mobile data
Journal article, 2020

This work aims at applying computational intelligence approaches to telecommunication data, in order to associate mobile data to energy consumption load curves. Clustering methods are applied in order to allow the telecommunication network to infer about its topology and consumption load forecasting. Through an extensive analysis of Telecom Italia dataset and power distribution lines data available for the city of Trento, it was possible to confirm the high correlation between them, mainly when voice data is considered. To a great extent, this correlation can be explained by the fact that cellular communication devices are physically present in the service area of the distribution lines and when people are communicating, they are also consuming energy. Based on the aforementioned dataset, load curves for the city of Trento were constructed having as inputs data from telecommunication transactions. Results show that it is possible to use the telecommunication load as the input to predict the energy load, with the proposed model performing better than the naive predictor in 82% of the tested distribution lines.

Energy forecasting

Mobile data

Learning

Smartphones

Clustering

Author

Frederico Coelho

Universidade Federal de Minas Gerais

Murilo Menezes

Universidade Federal de Minas Gerais

Lourenço Ribeiro

Universidade Federal de Minas Gerais

André Barbosa

Universidade Federal de Minas Gerais

Vinicius Silva

Universidade Federal de Minas Gerais

Antônio P. Braga

Universidade Federal de Minas Gerais

Carlos Natalino Da Silva

Chalmers, Electrical Engineering, Communication and Antenna Systems, Optical Networks

Paolo Monti

Chalmers, Electrical Engineering, Communication and Antenna Systems, Optical Networks

World Review of Science, Technology and Sustainable Development

1741-2242 (ISSN) 1741-2234 (eISSN)

Vol. 16 1 4-21

Subject Categories

Computer Engineering

Communication Systems

Bioinformatics (Computational Biology)

DOI

10.1504/WRSTSD.2020.105586

More information

Latest update

4/28/2020