Abstract
Smart meters that allow information to flow between users and utility service providers are expected to foster intelligent energy consumption. Previous studies focusing on demand-side management have been predominantly restricted to factors that utilities can manage and manipulate, but have ignored factors specific to residential characteristics. They also often presume that households consume similar amounts of energy and electricity. To fill these gaps in literature, the authors investigate two research questions: (RQ1) Does a data mining approach outperform traditional statistical approaches for modelling residential energy consumption? (RQ2) What factors influence household energy consumption? They identify household clusters to explore the underlying factors central to understanding electricity consumption behavior. Different clusters carry specific contextual nuances needed for fully understanding consumption behavior. The findings indicate electricity can be distributed according to the needs of six distinct clusters and that utilities can use analytics to identify load profiles for greater energy efficiency.
| Original language | English |
|---|---|
| Pages (from-to) | 166-193 |
| Number of pages | 28 |
| Journal | Journal of Global Information Management |
| Volume | 29 |
| Issue number | 2 |
| Early online date | 15 Feb 2021 |
| DOIs | |
| Publication status | Published - 1 Mar 2021 |
Bibliographical note
This article, published as an Open Access article on February 5th, 2021 in the gold Open Access journal, the Journal of Global InformationManagement(converted to gold Open Access January 1st, 2021), is distributed under the terms of the Creative Commons Attribution License
(http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and production in any medium, provided the
author of the original work and original publication source are properly credited.
Keywords
- Consumption pattern
- Data mining
- Modeling energy consumption
- Smart grid
- Smart meter