TY - GEN
T1 - A CDF-based symbolic time-series data mining approach for electricity consumption analysis
AU - Wu, I. Chin
AU - Chen, Yi An
AU - Wang, Zan Xian
N1 - Publisher Copyright:
© Springer International Publishing AG, part of Springer Nature 2018.
PY - 2018
Y1 - 2018
N2 - Electricity is critical for industrial and economic advancement, as well as a driving force for sustainable development. This study collects the energy consumption data of annealing processes from an annealing furnace of a co-operating steel forging plant. We propose a CDF-based symbolic time-series data mining and analytic framework for electricity consumption analysis and prediction of machine operating states by machine-learning techniques. We computed the breakpoint value relying on a density-based notion – namely, the cumulative distribution function (CDF) – to improve the original breakpoint table in the SAX algorithm for symbolizing the time-series data. The main contribution of this work is that the modified SAX algorithm can achieve better prediction the operating state of the machine in comparison to the original SAX algorithm.
AB - Electricity is critical for industrial and economic advancement, as well as a driving force for sustainable development. This study collects the energy consumption data of annealing processes from an annealing furnace of a co-operating steel forging plant. We propose a CDF-based symbolic time-series data mining and analytic framework for electricity consumption analysis and prediction of machine operating states by machine-learning techniques. We computed the breakpoint value relying on a density-based notion – namely, the cumulative distribution function (CDF) – to improve the original breakpoint table in the SAX algorithm for symbolizing the time-series data. The main contribution of this work is that the modified SAX algorithm can achieve better prediction the operating state of the machine in comparison to the original SAX algorithm.
KW - Cumulative distribution function
KW - Electricity consumption analysis
KW - Symbolic aggregate approximation
KW - Time-series data mining
UR - http://www.scopus.com/inward/record.url?scp=85061554037&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85061554037&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-92285-0_71
DO - 10.1007/978-3-319-92285-0_71
M3 - Conference contribution
AN - SCOPUS:85061554037
SN - 9783319922843
T3 - Communications in Computer and Information Science
SP - 515
EP - 521
BT - HCI International 2018 – Posters’ Extended Abstracts - 20th International Conference, HCI International 2018, Proceedings
A2 - Stephanidis, Constantine
PB - Springer Verlag
T2 - 20th International Conference on HCI, HCI International 2018
Y2 - 15 July 2018 through 20 July 2018
ER -
