Keele Research Repository
Explore the Repository
Chen, Q, Fan, Z, Kaleshi, D and Armour, S (2015) Rule Induction-Based Knowledge Discovery for Energy Efficiency. IEEE ACCESS, 3. 1423 - 1436. ISSN 2169-3536
07219372.pdf - Published Version
Available under License Creative Commons Attribution Non-commercial.
Download (4MB) | Preview
Abstract
Rule induction is a practical approach to knowledge discovery. Provided that a problem is developed, rule induction is able to return the knowledge that addresses the goal of this problem as if-then rules. The primary goals of knowledge discovery are for prediction and description. The rule format knowledge representation is easily understandable so as to enable users to make decisions. This paper presents the potential of rule induction for energy efficiency. In particular, three rule induction techniques are applied to derive knowledge from a dataset of thousands of Irish electricity customers’ time-series power consumption records, socio-demographic details, and other information, in order to address the following four problems: 1) discovering mathematically interesting knowledge that could be found useful; 2) estimating power consumption features for customers, so that personalized tariffs can be assigned; 3) targeting a subgroup of customers with high potential for peak demand shifting; and 4) identifying customer attitudes that dominate energy conservation.
Item Type: | Article |
---|---|
Additional Information: | This is the final published version of the article (version of record). It first appeared online via IEEE at http://doi.org/10.1109/ACCESS.2015.2472355 - please refer to any applicable terms of use of the publisher. |
Uncontrolled Keywords: | Energy efficiency, knowledge discovery, smart grids, subgroup discovery |
Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
Divisions: | Faculty of Natural Sciences > School of Computing and Mathematics |
Related URLs: | |
Depositing User: | Symplectic |
Date Deposited: | 11 May 2018 07:46 |
Last Modified: | 10 Feb 2021 16:08 |
URI: | https://eprints.keele.ac.uk/id/eprint/4880 |