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Title: Time series data mining for energy prices forecasting: an application to real data
Authors: e Silva, E. C.
Borges, A.
Teodoro, M. F.
Andrade, M. A. P.
Covas, R.
Keywords: Data mining
Electricity prices forecasting
Multivariate time series
Vector autoregressive models
Issue Date: 2017
Publisher: Springer
Abstract: Recently, at the 119th European Study Group with Industry, the Energy Solutions Operator EDP proposed a challenge concerning electricity prices simulation, not only for risk measures purposes but also for scenario analysis in terms of pricing and strategy. The main purpose was short-term Electricity Price Forecasting (EPF). This analysis is contextualized in the study of time series behavior, in particular multivariate time series, which is considered one of the current challenges in data mining. In this work a short-term EPF analysis making use of vector autoregressive models (VAR) with exogenous variables is proposed. The results show that the multivariate approach using VAR, with the season of the year and the type of day as exogenous variables, yield a model that explains the intra-day and intra-hour dynamics of the hourly prices.
Description: WOS:000406998500064 (Nº de Acesso Web of Science)
Peer reviewed: yes
DOI: 10.1007/978-3-319-53480-0_64
ISSN: 2194-5357
Publisher version: The definitive version is available at:
Appears in Collections:BRU-CRI - Comunicações a conferências internacionais

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