Analyzing and Predicting Power Consumption Profiles Using Big Data

Autor:	Osman Redondo Bilbao, Jairo Martínez Ventura, José Jinete Torres, Omar Bonerge Pineda Lezama, Hugo Hernández Palma, Jesús García Guiliany, Amelec Viloria, Ronald Prieto Pulido
Rok vydání:	2019
Předmět:	Euclidean distance Quadratic error Mean absolute percentage error Power consumption business.industry Big data Statistics Mean absolute error Predictive capability Autoregressive integrated moving average business Mathematics
Zdroj:	Communications in Computer and Information Science ISBN: 9789811513039 DependSys
DOI:	10.1007/978-981-15-1304-6_31
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::aaea739285ce2109372004263797cb7c https://doi.org/10.1007/978-981-15-1304-6_31 Zobrazit plný text záznamu