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Short‐term electricity load and price forecasting based on clustering and next symbol prediction
Author(s) -
Jin Cheng Hao,
Pok Gouchol,
Paik Incheon,
Ryu Keun Ho
Publication year - 2015
Publication title -
ieej transactions on electrical and electronic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 30
eISSN - 1931-4981
pISSN - 1931-4973
DOI - 10.1002/tee.22050
Subject(s) - cluster analysis , electricity , electricity price forecasting , term (time) , computer science , series (stratigraphy) , lossless compression , sequence (biology) , electricity market , data mining , time series , cluster (spacecraft) , symbol (formal) , data compression , artificial intelligence , machine learning , engineering , paleontology , physics , genetics , quantum mechanics , electrical engineering , biology , programming language
Short‐term electricity load and price forecasting is an important issue in competitive electricity markets. In this paper, we propose a new direct time series forecasting method based on clustering and next symbol prediction. First, the cluster label sequence is obtained from time series clustering. Then a lossless compression algorithm of prediction by partial match version C coder (PPMC) is applied on this obtained discrete cluster label sequence to predict the next cluster label. Finally, the whole time series values of one‐step‐ahead can be directly forecast from the predicted cluster label. The proposed method is evaluated on electricity time series datasets, and the numerical experiments show that the proposed method can achieve promising results in day‐ahead electricity load and price forecasting. © 2014 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.