Economic Interest of Heating and Hot Water Prediction System for Residential District
| Autor: | Luc Dufour, Dominique Genoud, Bruno Ladevie, Francesco Maria Cimmino, Stéphane Genoud, Jean-Jacques Bezian |
|---|---|
| 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 |
| Rok vydání: | 2016 |
| Předmět: |
Microgrid
Computer science 020209 energy KNIME Storage 02 engineering and technology 7. Clean energy Temperature measurement Water consumption Automotive engineering Data modeling [SPI]Engineering Sciences [physics] Virtual power plant 0202 electrical engineering electronic engineering information engineering Simulation Block (data storage) Water heating Heating consumption prediction Energy consumption AC power Predictive energy management Power (physics) Hot water consumption prediction Smart grid Energy Price Data intelligence analysis |
| Zdroj: | AINA Workshops 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), 2016, Crans-Montana, Switzerland. art.7471305-p. 827-832, ⟨10.1109/WAINA.2016.174⟩ |
| DOI: | 10.1109/waina.2016.174 |
| 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 which used supervised learning techniques. This paper presents an industrial methodology to predict heating and hot water consumption using time series analyzes and tree ensemble algorithm. 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. The results are based on the data collected in a building in Chamoson (Switzerland) and simulations. The aim is to provide to the virtual power plant the possibility to pilot an part of energy consumption. The input data for the pilot is the economic parameter. Considering the economic input data for the energy management, a new heasting and hot water consumption is provided for one week. |
| Databáze: | OpenAIRE |
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