Open AccessDay‐ahead electricity price analysis and forecasting by singular spectrum analysis
Author(s) -
Miranian Arash,
Abdollahzade Majid,
Hassani Hossein
Publication year - 2013
Publication title -
iet generation, transmission and distribution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.92
H-Index - 110
eISSN - 1751-8695
pISSN - 1751-8687
DOI - 10.1049/iet-gtd.2012.0263
Subject(s) - electricity price forecasting , singular spectrum analysis , electricity , electricity price , series (stratigraphy) , econometrics , time series , electricity market , computer science , noise (video) , probabilistic forecasting , electricity demand , electricity generation , economics , mathematical optimization , mathematics , artificial intelligence , engineering , power (physics) , machine learning , singular value decomposition , electrical engineering , probabilistic logic , image (mathematics) , biology , physics , quantum mechanics , paleontology
This study proposes a model‐free approach for day‐ahead electricity price forecasting. The proposed approached is based on the singular spectrum analysis (SSA) technique. The SSA is a relatively new and powerful technique in time series analysis and forecasting thanks to its well‐known capabilities in extracting the main structure of the broad classes of the time series. In this study, it is shown that SSA can be employed to decompose the original electricity price series into trend, periodic and noisy components. The main part of the price series, that is, the trend and harmonic components, is reconstructed by removing the noise component from the original series. The reconstructed price series is then used for forecasting the day‐ahead electricity prices. The proposed approach is evaluated by analysing and forecasting of the day‐ahead electricity prices in the Australian and Spanish electricity markets. The forecasting results confirm the superiority of the SSA approach compared with some of the recently published forecasting techniques.