Temporary Variables for Predicting Electricity Consumption Through Data Mining

• Autores: Silva, Jesús, Senior Naveda, Alexa, Hernández Palma, Hugo, Niebles Núẽz, William, Niebles Núẽz, Leonardo
• Formato: artículo
• Fecha de Publicación: 2020
• Institución: Universidad Peruana de Ciencias Aplicadas
• Repositorio: UPC-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorioacademico.upc.edu.pe:10757/652132
• Enlace del recurso: http://hdl.handle.net/10757/652132
• Nivel de acceso: acceso abierto
• Materia: Data mining; Electric power transmission networks; Electric power utilization; Forecasting; Electricity grids; Electricity-consumption; Intelligent distribution networks; Prediction systems; Real-time collection; Short term; Smart grid; Time variable

In the new global and local scenario, the advent of intelligent distribution networks or Smart Grids allows real-time collection of data on the operating status of the electricity grid. Based on this availability of data, it is feasible and convenient to predict consumption in the short term, from a few hours to a week. The hypothesis of the study is that the method used to present time variables to a prediction system of electricity consumption affects the results.