Title: Decision support system with business intelligence for thermal power plants

Authors: Kleyton Pontes Cotta; Angelo Marcelino Cordeiro; Camilla Barros Batista; Flávio Leite Loução Junior; Rodrigo José Silva De Almeida; Carlos Antônio Alves De Araújo Junior

Addresses: Department Research and Development, Radix Software and Engineering, Rio de Janeiro, RJ, Brazil ' Department Research and Development, Radix Software and Engineering, Rio de Janeiro, RJ, Brazil ' Department Research and Development, Radix Software and Engineering, Rio de Janeiro, RJ, Brazil ' Department Research and Development, Radix Software and Engineering, Rio de Janeiro, RJ, Brazil ' Paraíba Power Plants S.A. – EPASA, João Pessoa, PB, Brazil ' Paraíba Power Plants S.A. – EPASA, João Pessoa, PB, Brazil

Abstract: In this study, the development of a business intelligence (BI) application for a thermoelectric power plant is discussed. BI is a broad concept that can be applied in many different sectors. Most of the operation data is generated and evaluated in a very short time frame in a power plant, which can encumber the interpretation of power plant operations if analysing a larger time frame. The main idea for this application is to unify high-level overviews of variables from the energy production process, focusing on comparing real to optimised data, in order to better evaluate the difference between operation and the suggested optimisation. The main sources of information are power, consumption (of fuel), cost (of consumption), hours (of operation), and engines health indexes. The real data is extracted from physical sensors existent in each power generating unit, whilst the suggested optimised data is generated from models developed in research and development projects at the power plant. The application dashboards and datasets were entirely developed using Microsoft's Power BI Desktop, later being published online and shared among the company's decision-makers.

Keywords: business intelligence; data analytics; decision support systems; thermal power plants; TPPs.

DOI: 10.1504/IJBIS.2026.154004

International Journal of Business Information Systems, 2026 Vol.52 No.2, pp.240 - 257

Received: 14 Feb 2022
Accepted: 02 Sep 2022
Published online: 10 Jun 2026 *

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