Analyzing and predicting power consumption profiles using big data
Análisis y predicción de perfiles de consumo de energía utilizando big data
Date
2019
2019
Author
Amelec, Viloria
Prieto Pulido, Ronald Antonio
García Guiliany, Jesús
Martínez Ventura, Jairo
Hernández Palma, Hugo
Jinete Torres, José
Redondo Bilbao, Osman Enrique
Pineda Lezam, Omar Bonerge
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Abstract
The Euclidean distance (ED), the mean absolute error (MAE), the mean absolute percentage error (MAPE) and the root of the mean quadratic error (RMQE) are used to evaluate the predictive capability of the models supported by each statistical method, asserting, according to the assessment, that the best predictions come from the ARIMA method. This paper presents a prediction study for two buildings located at the University of Mumbai in India, in order to determine a method that fits the forecasts of organization expenses
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