An Electric Energy Consumer Characterization Framework Based on Data Mining Techniques
This paper presents an electricity consumer characterization framework based on a knowledge discovery in databases (KDD) procedure, supported by data mining (DM) techniques, applied on the different stages of the process. The core of this framework is a data mining model based on a combination of un...
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| Outros Autores: | , , |
| Formato: | article |
| Idioma: | eng |
| Publicado em: |
2017
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| Assuntos: | |
| Texto completo: | http://hdl.handle.net/10400.22/9979 |
| País: | Portugal |
| Oai: | oai:recipp.ipp.pt:10400.22/9979 |
| Resumo: | This paper presents an electricity consumer characterization framework based on a knowledge discovery in databases (KDD) procedure, supported by data mining (DM) techniques, applied on the different stages of the process. The core of this framework is a data mining model based on a combination of unsupervised and supervised learning techniques. Two main modules compose this framework: the load profiling module and the classification module. The load profiling module creates a set of consumer classes using a clustering operation and the representative load profiles for each class. The classification module uses this knowledge to build a classification model able to assign different consumers to the existing classes. The quality of this framework is illustrated with a case study concerning a real database of LV consumers from the Portuguese distribution company. |
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