Spillage Forecast in Hydroelectric Power Plants via Machine Learning

Pedro  H. M. Nascimento; Ramon  Abritta; Frederico  F. Panoeiro; Leonardo  de M. Honório; André  L. M. Marcato; Ivo  C. da Silva Junior

doi:10.48011/sbse.v1i1.2369

Pedro H. M. Nascimento Universidade Federal de Juiz de Fora
Ramon Abritta Universidade Federal de Juiz de Fora
Frederico F. Panoeiro Universidade Federal de Juiz de Fora
Leonardo de M. Honório Universidade Federal de Juiz de Fora
André L. M. Marcato Universidade Federal de Juiz de Fora
Ivo C. da Silva Junior Universidade Federal de Juiz de Fora

DOI: https://doi.org/10.48011/sbse.v1i1.2369

Keywords: Telemetry, Hydroelectric power, Machine learning, Forecasting, Resources managing

Abstract

Brazilian hydroelectric power plants often use telemetry stations to extract information about the environment. These equipment are usually installed in several strategic spots of rivers that "feed" the reservoir, and are capable of providing important information such as precipitation, river level, and water flow. This paper presents an analysis of Machine Learning applied to the forecasting of spillage occurrences over a set amount of time in a Brazilian power plant. To achieve this goal, telemetry stations' data were utilized together with the plant's operations historical, which provides information about previous spillages, turbines' flows, among others. The Machine Learning approach has shown to be promising in this problem, and the developed model presented the potential to eectively support decisions by helping the operators prepare for significant incoming water flows.