Fuzzy clustering and prediction of electricity demand based on household characteristics
Joaquim L. Viegas, Susana M. Vieira, João M. C. Sousa
Joaquim L. Viegas
Available Online June 2015.
- 10.2991/ifsa-eusflat-15.2015.147How to use a DOI?
- Fuzzy clustering, Fuzzy inference system, Smart meter data, Household energy consumption.
The electricity market has been significantly changing in the last decade. The deployment of smart meters is enabling the logging of huge amounts of data relating to the operations of utilities with the potential of being translated into valuable knowledge on the behaviour of consumers. This work proposes a methodology for predicting the typical daily load profile of electricity usage based on static data using fuzzy clustering and modelling. The methodology intends to: (1) determine consumer segments based on the metering data using the fuzzy c-means clustering algorithm, and (2) develop Takagi-Sugeno fuzzy models in order to predict the demand profile of the consumers.
- © 2015, the Authors. Published by Atlantis Press.
- Open Access
- This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).
Cite this article
TY - CONF AU - Joaquim L. Viegas AU - Susana M. Vieira AU - João M. C. Sousa PY - 2015/06 DA - 2015/06 TI - Fuzzy clustering and prediction of electricity demand based on household characteristics BT - Proceedings of the 2015 Conference of the International Fuzzy Systems Association and the European Society for Fuzzy Logic and Technology PB - Atlantis Press SP - 1040 EP - 1046 SN - 1951-6851 UR - https://doi.org/10.2991/ifsa-eusflat-15.2015.147 DO - 10.2991/ifsa-eusflat-15.2015.147 ID - Viegas2015/06 ER -