Abstract
An electric meter is used for measuring the electricity consumption in a household. The obtained total data doesn't separate the consumptions of individual appliances and provides little actionable information for energy efficiency improvement. This paper presents an enhanced Iterative Self-Organizing Data Analysis (ISODATA) algorithm which can identify the operating modes and electricity consumptions of major appliances based on the metered total electricity consumption. The enhanced ISODATA algorithm is improved with k-medoids and parameter selections. It can overcome various drawbacks of the original k-means and the original ISODATA algorithm. Validated with actual data, the presented method is able to disaggregate the total electricity consumption efficiently and accurately into categories corresponding to major end uses.
Original language | English |
---|---|
Pages (from-to) | 305-316 |
Number of pages | 12 |
Journal | Energy and Buildings |
Volume | 140 |
DOIs | |
Publication status | Published - 1 Apr 2017 |
Keywords
- Clustering
- Electrical appliance
- ISODATA
- Pattern recognition
- Unsupervised learning
ASJC Scopus subject areas
- Civil and Structural Engineering
- Building and Construction
- Mechanical Engineering
- Electrical and Electronic Engineering