Stream Learning in Energy IoT Systems: A Case Study in Combined Cycle Power Plants

Jesus L. Lobo, Igor Ballesteros, Izaskun Oregi, Javier Del Ser and Sancho Salcedo-Sanz
Additional contact information
Jesus L. Lobo: TECNALIA, Basque Research and Technology Alliance (BRTA), 48160 Derio-Bizkaia, Spain
Igor Ballesteros: University of the Basque Country UPV/EHU, 48013 Bilbao, Spain
Izaskun Oregi: TECNALIA, Basque Research and Technology Alliance (BRTA), 48160 Derio-Bizkaia, Spain
Javier Del Ser: TECNALIA, Basque Research and Technology Alliance (BRTA), 48160 Derio-Bizkaia, Spain
Sancho Salcedo-Sanz: Department of Signal Processing and Communications, University of Alcalá, E-28871 Alcalá de Henares, Spain

Energies, 2020, vol. 13, issue 3, 1-28

Abstract: The prediction of electrical power produced in combined cycle power plants is a key challenge in the electrical power and energy systems field. This power production can vary depending on environmental variables, such as temperature, pressure, and humidity. Thus, the business problem is how to predict the power production as a function of these environmental conditions, in order to maximize the profit. The research community has solved this problem by applying Machine Learning techniques, and has managed to reduce the computational and time costs in comparison with the traditional thermodynamical analysis. Until now, this challenge has been tackled from a batch learning perspective, in which data is assumed to be at rest, and where models do not continuously integrate new information into already constructed models. We present an approach closer to the Big Data and Internet of Things paradigms, in which data are continuously arriving and where models learn incrementally, achieving significant enhancements in terms of data processing (time, memory and computational costs), and obtaining competitive performances. This work compares and examines the hourly electrical power prediction of several streaming regressors, and discusses about the best technique in terms of time processing and predictive performance to be applied on this streaming scenario.

Keywords: electrical power prediction; combined cycle power plant; stream learning; online regression (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/13/3/740/pdf (application/pdf)
https://www.mdpi.com/1996-1073/13/3/740/ (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:jeners:v:13:y:2020:i:3:p:740-:d:318059

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

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