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Data-Driven Machine-Learning Model in District Heating System for Heat Load Prediction: A Comparison Study
Joint Authors
Dalipi, Fisnik
Yildirim Yayilgan, Sule
Gebremedhin, Alemayehu
Source
Applied Computational Intelligence and Soft Computing
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-06-15
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Information Technology and Computer Science
Abstract EN
We present our data-driven supervised machine-learning (ML) model to predict heat load for buildings in a district heating system (DHS).
Even though ML has been used as an approach to heat load prediction in literature, it is hard to select an approach that will qualify as a solution for our case as existing solutions are quite problem specific.
For that reason, we compared and evaluated three ML algorithms within a framework on operational data from a DH system in order to generate the required prediction model.
The algorithms examined are Support Vector Regression (SVR), Partial Least Square (PLS), and random forest (RF).
We use the data collected from buildings at several locations for a period of 29 weeks.
Concerning the accuracy of predicting the heat load, we evaluate the performance of the proposed algorithms using mean absolute error (MAE), mean absolute percentage error (MAPE), and correlation coefficient.
In order to determine which algorithm had the best accuracy, we conducted performance comparison among these ML algorithms.
The comparison of the algorithms indicates that, for DH heat load prediction, SVR method presented in this paper is the most efficient one out of the three also compared to other methods found in the literature.
American Psychological Association (APA)
Dalipi, Fisnik& Yildirim Yayilgan, Sule& Gebremedhin, Alemayehu. 2016. Data-Driven Machine-Learning Model in District Heating System for Heat Load Prediction: A Comparison Study. Applied Computational Intelligence and Soft Computing،Vol. 2016, no. 2016, pp.1-11.
https://search.emarefa.net/detail/BIM-1094898
Modern Language Association (MLA)
Dalipi, Fisnik…[et al.]. Data-Driven Machine-Learning Model in District Heating System for Heat Load Prediction: A Comparison Study. Applied Computational Intelligence and Soft Computing No. 2016 (2016), pp.1-11.
https://search.emarefa.net/detail/BIM-1094898
American Medical Association (AMA)
Dalipi, Fisnik& Yildirim Yayilgan, Sule& Gebremedhin, Alemayehu. Data-Driven Machine-Learning Model in District Heating System for Heat Load Prediction: A Comparison Study. Applied Computational Intelligence and Soft Computing. 2016. Vol. 2016, no. 2016, pp.1-11.
https://search.emarefa.net/detail/BIM-1094898
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1094898