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Subspace Clustering and Temporal Mining for Wind Power Forecasting
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- Authors
- Heon Gyu Lee, and Yong Ho Shin
- Issue Date
- 2014-03
- Citation
- International Journal of Advances in Soft Computing and its Applications, v.6, no.1, pp.1-21
- ISSN
- 2074-8523
- Language
- English
- Type
- Journal Article
- Abstract
- Wind power energy has received the biggest attention among the new renewable energies. For achieving a stable power generation from wind energy, the accurate analysis and forecasting of wind power pattern is required. In this paper, we propose subspace clustering method for generating clusters of similar wind power patterns from data to be analyzed and the calendar-based temporal associative classification rule mining for reflecting temporal information of wind power on the classification/prediction model. The experiments show that the optimal cluster is constructed by applying PROCLUS algorithm and it has 88.6% accuracy of prediction under application of temporal associative classification rules.
- KSP Keywords
- Classification rule mining, Clustering method, Power and energy, Power generation, Temporal mining, Wind energy, accuracy of prediction, associative classification, power pattern, prediction model, renewable energy(RE)