Journal
JOURNAL OF CLEANER PRODUCTION
Volume 203, Issue -, Pages 810-821Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.jclepro.2018.08.207
Keywords
Artificial intelligence; Extra trees; Random forest; Decision trees; Ensemble algorithms; Solar thermal energy systems
Categories
Funding
- Horizon 2020 project PENTAGON Unlocking European grid local flexibility trough augmented energy conversion capabilities at district-level [731125]
- European Commission
- H2020 Societal Challenges Programme [731125] Funding Source: H2020 Societal Challenges Programme
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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. (C) 2018 The Authors. Published by Elsevier Ltd.
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