Title :
Rule Induction-Based Knowledge Discovery for Energy Efficiency
Author :
Qipeng Chen ; Zhong Fan ; Kaleshi, Dritan ; Armour, Simon
Author_Institution :
Dept. of Electr. & Electron. Eng., Univ. of Bristol, Bristol, UK
fDate :
7/7/1905 12:00:00 AM
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.
Keywords :
data mining; energy conservation; power consumption; power engineering computing; time series; Irish electricity customers; customer attitudes; energy conservation; energy efficiency; if-then rules; knowledge discovery; peak demand shifting; rule format knowledge representation; rule induction techniques; socio-demographic details; time-series power consumption records; Energy efficiency; Knowledge discovery; Smart grids; Energy efficiency; energy efficiency; knowledge discovery; smart grids; subgroup discovery;
Journal_Title :
Access, IEEE
DOI :
10.1109/ACCESS.2015.2472355