Heating and hot water industrial prediction system for residential district

Dufour, Luc (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Genoud, Dominique (University of Applied Sciences and Arts Western Switzerland (HES-SO Valais-Wallis)) ; Ladevie, Bruno (Mines-Telecom, Albi, France) ; Bezian, Jean-Jacques (Mines-Telecom, Albi, France)

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.


Mots-clés:
Type de conférence:
full paper
Faculté:
Economie et Services
Ecole:
HEG VS HES-SO Valais-Wallis - Haute Ecole de Gestion & Tourisme
Institut:
Institut Informatique de gestion
Classification:
Informatique
Adresse bibliogr.:
Crans-Montana, Suisse, 23-25 March 2016
Date:
Crans-Montana, Suisse
23-25 March 2016
2016
Pagination:
6 p.
Publié dans
Proceedings of the 30th IEEE International Conference on Advanced Information Networking and Applications (AINA) 2016
DOI:
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 Notice créée le 2016-09-28, modifiée le 2018-08-31

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