Predicting Heating/Cooling Loads with the Zoetrope Genetic Programming (ZGP) Versus Other Machine Learning Methods

Document Type

Book Chapter

Source of Publication

Lecture Notes in Networks and Systems

Publication Date

8-1-2024

Abstract

This paper presents a comparison between a relatively new regression model called Zoetrope Genetic Programming (ZGP) and traditional machine learning techniques such as Linear Regression, Random Forest, Support Vector Classifier, and Multi Linear Perceptron. The application is a challenging heat load prediction problem with a real data set selected. The ZGP showed comparative results and was in second place for most of the metrics used. The Random forest still showed the best results. Analysis and justifications are shown in the rest of the paper.

ISBN

978-3-031-66335-2, 978-3-031-66336-9

ISSN

2367-3389

Publisher

Springer Nature Switzerland

Volume

1068

First Page

386

Last Page

398

Disciplines

Computer Sciences

Keywords

Zoetrope Genetic Programming, Heating/Cooling Load Prediction, Machine Learning Comparison, Regression Models, Random Forest

Indexed in Scopus

no

Open Access

no

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