TY - GEN
T1 - Analyzing load profiles of electricity consumption by a time series data mining framework
AU - Wu, I. Chin
AU - Chen, Tzu Li
AU - Chen, Yen Ming
AU - Liu, Tzu Chi
AU - Chen, Yi An
N1 - Publisher Copyright:
© Springer International Publishing AG 2017.
PY - 2017
Y1 - 2017
N2 - Given the problems of gradual oil depletion and global warming, energy consumption has become a critical factor for energy-intensive sectors, especially the semiconductor, manufacturing, iron and steel, and aluminum industries. In turn, reducing energy consumption for sustainability and both tracking and managing energy efficiently have become critical challenges. In response, we analyzed electricity consumption from the perspective of load profiling, which charts variation in electrical load during a specified period in order to track energy consumption. As a result, we proposed a time series data mining and analytic framework for electricity consumption analysis and pattern extraction by streaming data mining and machine learning techniques. We identified key factors to predict the state of the annealing furnace and detect abnormal patterns of the load profile of their electricity consumption. Our experimental results show that the dimension reduction method known as piecewise aggregate approximation can help to detect the state of the annealing furnace.
AB - Given the problems of gradual oil depletion and global warming, energy consumption has become a critical factor for energy-intensive sectors, especially the semiconductor, manufacturing, iron and steel, and aluminum industries. In turn, reducing energy consumption for sustainability and both tracking and managing energy efficiently have become critical challenges. In response, we analyzed electricity consumption from the perspective of load profiling, which charts variation in electrical load during a specified period in order to track energy consumption. As a result, we proposed a time series data mining and analytic framework for electricity consumption analysis and pattern extraction by streaming data mining and machine learning techniques. We identified key factors to predict the state of the annealing furnace and detect abnormal patterns of the load profile of their electricity consumption. Our experimental results show that the dimension reduction method known as piecewise aggregate approximation can help to detect the state of the annealing furnace.
KW - Energy consumption analysis
KW - Load profiling
KW - Piecewise aggregate approximation
KW - Time-series data mining
UR - http://www.scopus.com/inward/record.url?scp=85025151051&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85025151051&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-58484-3_35
DO - 10.1007/978-3-319-58484-3_35
M3 - Conference contribution
AN - SCOPUS:85025151051
SN - 9783319584836
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 443
EP - 454
BT - HCI in Business, Government and Organizations
A2 - Tan, Chuan-Hoo
A2 - Nah, Fiona Fui-Hoon
PB - Springer Verlag
T2 - 14th International Conference on Logic Programming and Nonmonotonic Reasoning, LPNMR 2017
Y2 - 3 July 2017 through 6 July 2017
ER -
