Repository logo
 
No Thumbnail Available
Publication

Electricity demand profile prediction based on household characteristics

Use this identifier to reference this record.
Name:Description:Size:Format: 
VMFMendes.pdf658.73 KBAdobe PDF Download

Advisor(s)

Abstract(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.

Description

Keywords

Data mining Machine learning Smart meter data Household energy consumption Segmentation

Citation

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

Research Projects

Organizational Units

Journal Issue

Publisher

Institute of Electrical and Electronics Engineers

CC License

Altmetrics