Authors:
R. Rueda
;
M. P. Cuéllar
;
M. Delgado
and
M. C. Pegalajar
Affiliation:
University of Granada, Spain
Keyword(s):
Energy Efficiency, Genetic Programming, Straight Line Programs, Pattern Recognition.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Economics, Business and Forecasting Applications
;
Evolutionary Computation
;
Exact and Approximate Inference
;
Knowledge Acquisition and Representation
;
Learning of Action Patterns
;
Pattern Recognition
;
Regression
;
Software Engineering
;
Theory and Methods
Abstract:
In the last few years, energy efficiency has become a research field of high interest for governments and industry. In order to understand consumption data and provide useful information for high-level decision making processes in energy efficiency, there is the problem of information modelling and knowledge discovery coming from a set of energy consumption sensors. This paper focuses in this problem, and explores the use of symbolic regression techniques able to find out patterns in data that can be used to extract an analytical formula that explains the behaviour of energy consumption in a set of public buildings. More specifically, we test the feasibility of different representations such as trees and straight line programs for the implementation of genetic programming algorithms, to find out if a building consumption data can be suitably explained from the energy consumption data from other similar buildings. Our experimental study suggests that the Straight Line Programs represe
ntation may overcome the limitations of traditional tree-based representations and provides accurate patterns of energy consumption models.
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