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Spatio-Temporal Mining for Power Load Forecasting in GIS-AMR Load Analysis Model
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- Authors
- Heon Gyu Lee, Yong Hoon Choi, Jin Ho Shin
- Issue Date
- 2009-11
- Citation
- International Conference on Interaction Sciences: Information Technology, Culture and Human (ICIS) 2009, pp.1201-1206
- Publisher
- ACM
- Language
- English
- Type
- Conference Paper
- Abstract
- A spatio-temporal mining technique is used to predict power load patterns for a voltage transformer. It is applied from load data measured every thirty minutes and a GIS-AMR database collected by a transformer's load measurement system over a wireless network. The proposed approach in this paper consists of three stages, (i) data preprocessing: noise or outlier is removed and the continuous attribute-valued features are transformed to new features (feature extraction and discretization), (ii) cluster analysis: SOMs (Self Organizing Maps) clustering is used to label the class and (iii) classification: we used and evaluated classification rules using spatio-temporal mining to build a suitable load forecasting model. In order to evaluate the result of classification, derived class labels from clustering and other features are used as input to build classification rules including time and spatial factors. Lastly, the result of our experiments is presented. Copyright © 2009 ACM.
- KSP Keywords
- Analysis Model, Classification Rule, Cluster analysis(CA), Continuous attribute, Data Preprocessing, Feature extractioN, Load Analysis, Load data, Load measurement, Power load forecasting, Self-organizing Map