@inproceedings{e4b9a8e4b61440fd9d2c49b3b326196e,
title = "Analyzing load profiles of electricity consumption by a time series data mining framework",
abstract = "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.",
keywords = "Energy consumption analysis, Load profiling, Piecewise aggregate approximation, Time-series data mining",
author = "Wu, {I. Chin} and Chen, {Tzu Li} and Chen, {Yen Ming} and Liu, {Tzu Chi} and Chen, {Yi An}",
year = "2017",
month = jan,
day = "1",
doi = "10.1007/978-3-319-58484-3_35",
language = "English",
isbn = "9783319584836",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "443--454",
editor = "Chuan-Hoo Tan and Nah, {Fiona Fui-Hoon}",
booktitle = "HCI in Business, Government and Organizations",
note = "14th International Conference on Logic Programming and Nonmonotonic Reasoning, LPNMR 2017 ; Conference date: 03-07-2017 Through 06-07-2017",
}