Using Industry 4.0’s Big Data and IoT to Perform Feature-Based and Past Data-Based Energy Consumption Predictions

Jonathan Gumz, Diego Castro Fettermann (), Enzo Morosini Frazzon and Mirko Kück
Additional contact information
Jonathan Gumz: Department of Production Engineering, Universidade Federal de Santa Catarina, Florianópolis 88040-900, Brazil
Diego Castro Fettermann: Department of Production Engineering, Universidade Federal de Santa Catarina, Florianópolis 88040-900, Brazil
Enzo Morosini Frazzon: Department of Production Engineering, Universidade Federal de Santa Catarina, Florianópolis 88040-900, Brazil
Mirko Kück: Department of Production Engineering, Universität Bremen, 28359 Bremen, Germany

Sustainability, 2022, vol. 14, issue 20, 1-34

Abstract: Industry 4.0 and its technologies allow advancements in communications, production and management efficiency across several segments. In smart grids, essential parts of smart cities, smart meters act as IoT devices that can gather data and help the management of the sustainable energy matrix, a challenge that is faced worldwide. This work aims to use smart meter data and household features data to seek the most appropriate methods of energy consumption prediction. Using the Cross-Industry Standard Process for Data Mining (CRISP-DM) method, Python Platform, and several prediction methods, prediction experiments were performed with household feature data and past consumption data of over 470 smart meters that gathered data for three years. Support vector machines, random forest regression, and neural networks were the best prediction methods among the ones tested in the sample. The results help utilities (companies that maintain the infrastructure for public services) to offer better contracts to new households and to manage their smart grid infrastructure based on the forecasted demand.

Keywords: smart meters; forecasting; python; support vector machines; random forest; neural networks (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/14/20/13642/pdf (application/pdf)
https://www.mdpi.com/2071-1050/14/20/13642/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:20:p:13642-:d:949314

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().