Using a business intelligence framework and data mining as computational tools in SMEs: Production forecasting of a hydroelectric power plant as a case study (#832)

Authors

Risco-Ramos, Redy H.

Pérez-Aguilar, Daniel A.

Casaverde-Pacherrez, Luis A.

Malpica Rodríguez, Manuel

Pérez-Aguilar, Jair M.

Pérez-Aguilar, Andy P.

Abstract

The aim of this paper is to demonstrate the utility of using the CRISP-DM (Cross-Industry Standard Process for Data Mining) reference model as a data mining methodology within a commercial business intelligence (BI) platform for Small and Medium Enterprises (SMEs). To achieve this objective, the CRISP-DM model was applied, the Power BI application was used as a BI platform, and the methodology was implemented in a specific use case study within a hydraulic generation SME. The results show that the methodology can be easily applied with readily available hardware and software, resulting in better outcomes compared to conventional practices used in the company. Based on these results, it can be concluded that the method used delivers useful results, it is feasible to implement with accessible hardware and software for SMEs, and the reference model remains valid when the data mining objectives are well-defined.