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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Haoyang Shen Hino, H. Murata, N. Wakao, S. |
| Copyright Year | 2011 |
| Abstract | Solar power, wind power, and co-generation (combined heat and power) systems are possible candidate for household power generation. These systems have their advantages and disadvantages. To propose the optimal combination of the power generation systems, the extraction of basic patterns of energy consumption of the house is required. In this study, energy consumption patterns are modeled by mixtures of Gaussian distributions. Then, using the symmetrized Kullback-Leibler divergence as a distance measure of the distributions, the basic pattern of energy consumption is extracted by means of hierarchical clustering. By an experiment using the Annex 42 dataset, it is shown that the proposed method is able to extract typical energy consumption patterns. |
| Starting Page | 275 |
| Ending Page | 280 |
| File Size | 906466 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781457721342 |
| DOI | 10.1109/ICMLA.2011.68 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-12-18 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Energy consumption Electricity Renewable energy resources energy consumption pattern Gaussian distribution Wind power generation KL-divergence hierarchical clustering Data models Approximation methods Gaussian mixture model |
| Content Type | Text |
| Resource Type | Article |
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