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Predictive modelling for solar thermal energy systems: A comparison of support vector regression, random forest, extra trees and regression trees

Ahmad, Muhammad, Reynolds, Jonathan and Rezgui, Yacine 2018. Predictive modelling for solar thermal energy systems: A comparison of support vector regression, random forest, extra trees and regression trees. Journal of Cleaner Production 203 , pp. 810-821. 10.1016/j.jclepro.2018.08.207

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Abstract

Predictive analytics play an important role in the management of decentralised energy systems. Prediction models of uncontrolled variables (e.g., renewable energy sources generation, building energy consumption) are required to optimally manage electrical and thermal grids, making informed decisions and for fault detection and diagnosis. The paper presents a comprehensive study to compare tree-based ensemble machine learning models (random forest – RF and extra trees – ET), decision trees (DT) and support vector regression (SVR) to predict the useful hourly energy from a solar thermal collector system. The developed models were compared based on their generalisation ability (stability), accuracy and computational cost. It was found that RF and ET have comparable predictive power and are equally applicable for predicting useful solar thermal energy (USTE), with root mean square error (RMSE) values of 6.86 and 7.12 on the testing dataset, respectively. Amongst the studied algorithms, DT is the most computationally efficient method as it requires significantly less training time. However, it is less accurate (RMSE = 8.76) than RF and ET. The training time of SVR was 1287.80 ms, which was approximately three times higher than the ET training time.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Engineering
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Publisher: Elsevier
ISSN: 0959-6526
Funders: European Commission
Date of First Compliant Deposit: 6 September 2018
Date of Acceptance: 19 August 2018
Last Modified: 11 Sep 2018 12:55
URI: http://orca-mwe.cf.ac.uk/id/eprint/114635

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