Irmler, Robert ; Rüppel, Uwe
Hrsg.: Li, Haijiang ; de Wilde, Pieter ; Rafiq, Yaqub (2014)
Comparison between support vector regression and grey system theory based models for adaptive load forecasting of building thermal energy consumption.
21st EG-ICE International Workshop. Cardiff, United Kingdom (16.07.2014-18.07.2014)
Konferenzveröffentlichung, Bibliographie
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: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2014 |
| Herausgeber: | Li, Haijiang ; de Wilde, Pieter ; Rafiq, Yaqub |
| Autor(en): | Irmler, Robert ; Rüppel, Uwe |
| Art des Eintrags: | Bibliographie |
| Titel: | Comparison between support vector regression and grey system theory based models for adaptive load forecasting of building thermal energy consumption |
| Sprache: | Englisch |
| Publikationsjahr: | Juli 2014 |
| Buchtitel: | Proceedings of the 21st International Workshop on Intelligent Computing in Engineering |
| Veranstaltungstitel: | 21st EG-ICE International Workshop |
| Veranstaltungsort: | Cardiff, United Kingdom |
| Veranstaltungsdatum: | 16.07.2014-18.07.2014 |
| URL / URN: | http://egice2014.engineering.cf.ac.uk/images/BIN/papers/32.p... |
| Zugehörige Links: | |
| 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. |
| Zusätzliche Informationen: | ISBN: 978-0-9930807-0-8 |
| Fachbereich(e)/-gebiet(e): | 13 Fachbereich Bau- und Umweltingenieurwissenschaften 13 Fachbereich Bau- und Umweltingenieurwissenschaften > Institut für Numerische Methoden und Informatik im Bauwesen |
| Hinterlegungsdatum: | 20 Jan 2015 15:43 |
| Letzte Änderung: | 04 Jan 2021 14:06 |
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