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Comparison between support vector regression and grey system theory based models for adaptive load forecasting of building thermal energy consumption

Irmler, Robert ; Rüppel, Uwe
Li, Haijiang ; de Wilde, Pieter ; Rafiq, Yaqub (eds.) :

Comparison between support vector regression and grey system theory based models for adaptive load forecasting of building thermal energy consumption.
[Online-Edition: http://egice2014.engineering.cf.ac.uk/images/BIN/papers/32.p...]
In: 21st EG-ICE International Workshop, 16.-18. Juli 2014, Cardiff, United Kingdom. Proceedings of the 21st International Workshop on Intelligent Computing in Engineering
[Konferenz- oder Workshop-Beitrag] , (2014)

Offizielle URL: http://egice2014.engineering.cf.ac.uk/images/BIN/papers/32.p...

Kurzbeschreibung (Abstract)

The prediction of building thermal energy consumption plays an important role in building energy management systems. Aside from physical simulations a common approach to predict such energy consumptions is to use machine learning methods like support vector regression (SVR). In addition, there is another group of models that are called ‘grey models’. Grey models are based on the so-called ‘grey system theory’, a relatively new branch of mathematical theory, and are designed to be able to handle systems that are characterized by little and uncertain information. This paper aims on investigating the performance of those models, regarding the adaptive load forecasting of building thermal energy consumption. Therefore, a GM(1,1) grey model is used to forecast building thermal energy consumption using a sliding window technique and synthetic real time measurements. Finally, the results are compared with the results of a ε-SVR-model working under similar conditions.

Typ des Eintrags: Konferenz- oder Workshop-Beitrag (Keine Angabe)
Erschienen: 2014
Herausgeber: Li, Haijiang ; de Wilde, Pieter ; Rafiq, Yaqub
Autor(en): Irmler, Robert ; Rüppel, Uwe
Titel: Comparison between support vector regression and grey system theory based models for adaptive load forecasting of building thermal energy consumption
Sprache: Englisch
Kurzbeschreibung (Abstract):

The prediction of building thermal energy consumption plays an important role in building energy management systems. Aside from physical simulations a common approach to predict such energy consumptions is to use machine learning methods like support vector regression (SVR). In addition, there is another group of models that are called ‘grey models’. Grey models are based on the so-called ‘grey system theory’, a relatively new branch of mathematical theory, and are designed to be able to handle systems that are characterized by little and uncertain information. This paper aims on investigating the performance of those models, regarding the adaptive load forecasting of building thermal energy consumption. Therefore, a GM(1,1) grey model is used to forecast building thermal energy consumption using a sliding window technique and synthetic real time measurements. Finally, the results are compared with the results of a ε-SVR-model working under similar conditions.

Buchtitel: Proceedings of the 21st International Workshop on Intelligent Computing in Engineering
Fachbereich(e)/-gebiet(e): 13 Fachbereich Bau- und Umweltingenieurwissenschaften
13 Fachbereich Bau- und Umweltingenieurwissenschaften > Institut für Numerische Methoden und Informatik im Bauwesen
Veranstaltungstitel: 21st EG-ICE International Workshop
Veranstaltungsort: Cardiff, United Kingdom
Veranstaltungsdatum: 16.-18. Juli 2014
Hinterlegungsdatum: 20 Jan 2015 15:43
Offizielle URL: http://egice2014.engineering.cf.ac.uk/images/BIN/papers/32.p...
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ISBN: 978-0-9930807-0-8

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