Paper Info
Title
Spatio-temporal mining for power load forecasting in GIS-AMR load analysis model
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.
Year
DOI
Venue
2009
10.1145/1655925.1656144
Int. Conf. Interaction Sciences
Keywords
Field
DocType
spatio-temporal mining,voltage transformer,gis-amr database,gis-amr load analysis model,spatio-temporal mining technique,load data,power load forecasting,class label,power load pattern,suitable load forecasting model,classification rule,load measurement system,data preprocessing,wireless network,measurement system,pattern analysis,feature extraction,cluster analysis
Data mining,Wireless network,Discretization,Computer science,Transformer,Outlier,Data pre-processing,Feature extraction,Self-organizing map,Cluster analysis
Conference
Citations 
PageRank 
References 
0
0.34
6
Authors
3
Name
Order
Citations
PageRank
Heon Gyu Lee1727.77
Yong-hoon Choi212923.81
Jin-Ho Shin34911.31