| Nome: | Descrição: | Tamanho: | Formato: | |
|---|---|---|---|---|
| 658.73 KB | Adobe PDF |
Orientador(es)
Resumo(s)
This work proposes a methodology for predicting the typical daily load profile of electricity usage based on static data obtained from surveys. The methodology intends to: (1) determine consumer segments based on the metering data using the k-means clustering algorithm, (2) correlate survey data to the segments, and (3) develop statistical and machine learning classification models to predict the demand profile of the consumers. The developed classification models contribute to make the study and planning of demand side management programs easier, provide means for studying the impact of alternative tariff setting methods and generate useful knowledge for policy makers.
Descrição
Palavras-chave
Data mining Machine learning Smart meter data Household energy consumption Segmentation
Contexto Educativo
Citação
Electricity demand profile prediction based on household characteristics. In 2015 12th International Conference on the European Energy Market (EEM). Lisbon, Portugal: IEEE, 2015. ISBN 978-1-4673-6692-2. Pp. 1-5
Editora
Institute of Electrical and Electronics Engineers