Knowledge extraction from time series of electric energy demand using temporal data mining
Alynne Conceição Saraiva Queiroz, José Alfredo Ferreira Costa · 2017
Planning activities are very important in the energy sector, where the utilities are seeking information that may assist in decisions regarding expansion needs and resource management, improving the quality of their services. This paper presents a methodology based on mining tools and representation of time series, in order to extract knowledge from series of electricity demand in various substations connected to an energy provider. To represent this knowledge, the language proposed by Mörchen (2005) called Time Series Knowledge Representation (TSKR) is used. It was conducted a case study using time series of energy demand for 8 substations interconnected by a ring system, which feeds the metropolitan area of Goiania-GO (Brazil), provided by CELG (Companhia Energética de Goiás), responsible for the service of power distribution in the state of Goiás (Brazil). Using the proposed methodology, three levels of knowledge that describe the behavior of the studied system were extracted, representing clearly the system dynamics, thus becoming a tool to assist planning activities.