Heating and hot water industrial prediction system for residential district
| Autor: | Dominique Genoud, Jean-Jacques Bezian, Luc Dufour, Bruno Ladevie |
|---|---|
| Přispěvatelé: | Centre de recherche d'Albi en génie des procédés des solides divisés, de l'énergie et de l'environnement (RAPSODEE), Centre National de la Recherche Scientifique (CNRS)-IMT École nationale supérieure des Mines d'Albi-Carmaux (IMT Mines Albi), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Barolli, L and Takizawa, M and Enokido, T and Jara, AJ and Bocchi |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Microgrid
Computer science KNIME Storage 02 engineering and technology 7. Clean energy Automotive engineering Water consumption Data modeling [SPI]Engineering Sciences [physics] 0202 electrical engineering electronic engineering information engineering Simulation Block (data storage) Water heating 020208 electrical & electronic engineering Heating consumption prediction Predictive energy management Power (physics) Hot water consumption prediction Tree (data structure) Smart grid Work (electrical) Data intelligence analysis 020201 artificial intelligence & image processing Low voltage |
| Zdroj: | IEEE AINA-30th International Conference on Advanced Information Networking and Applications-WORKSHOPS (WAINA 2016) IEEE AINA-30th International Conference on Advanced Information Networking and Applications-WORKSHOPS (WAINA 2016), Mar 2016, Crans-Montana, Switzerland. art. 7471304-p. 821-826, ⟨10.1109/WAINA.2016.173⟩ AINA Workshops |
| DOI: | 10.1109/WAINA.2016.173⟩ |
| Popis: | International audience; This work presents a data-intensive solution to predict heating and hot water consumption. The ability to predict locally those flexible sources considering meteorological uncertainty can play a key role in the management of microgrid. A microgrid is a building block of future smart grid, it can be defined as a network of low voltage power generating units, storage devices and loads. The main novelties of our approach is to provide an easy implemented and flexible solution that used a supervised learning techniques. This paper presents an industrial methodology to predict heating and hot water consumption using time series analyzes and tree ensemble algorithm. The results are based on the data collected in a building in Chamoson (Switzerland) and simulations. Considering the winter season 2012-2013 for the training, the heating and hot water predictions is correctly estimated 90% +/- 1.2 for the winter season 2013-2014. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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