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Saving energy by anticipating hot water production: identification of key points for an efficient statistical model integration

Published online by Cambridge University Press:  06 May 2019

Yvan Denis ,
Frédéric Suard ,
Aurore Lomet  and
David Chèze
Yvan Denis*
Affiliation:
LIANES, CEA, INES 50 avenue du lac Léman, Technopôle Savoie Technolac, Le Bourget du Lac Cedex 73375, France
Frédéric Suard
Affiliation:
LIANES, CEA, INES 50 avenue du lac Léman, Technopôle Savoie Technolac, Le Bourget du Lac Cedex 73375, France
Aurore Lomet
Affiliation:
LIST, CEA, Gif-sur-Yvette F-91191, France
David Chèze
Affiliation:
LSTB, CEA, INES 50 avenue du lac Léman, Technopôle Savoie Technolac, Le Bourget du Lac Cedex 73375, France
*
Author for correspondence: Yvan Denis, E-mail: yvandeni@gmail.com
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Abstract

This work aims to evaluate the energy savings that can be achieved in domestic hot water (DHW) production using consumption forecasting through statistical modeling. It uses our forecast algorithm and aims at investigating how it can improve energy efficiency depending on the system configuration. Especially, the influence of the DHW production type used is evaluated as well as the water tank insulation. To that end, real consumption measurements are used for model training. Then simulations are run on using TRNSYS software to compute the total energy consumption of DHW production systems over 1 year. Simulations are also based on real consumption measurements for realistic results. To appraise the energy savings, we compared simulations that consider either no forecast (reactive control), perfect forecast (to estimate the control ability to consider forecast), or the forecast provided by our algorithm. The measurements and simulations are run on 26 different but real dwellings to assess results variability. Several system configurations are also compared with varying thermal insulation indices for a complete benchmark of the approach so that an overall performance of the system and the anticipation strategy could be evaluated.

Keywords

ARIMAconsumption forecastECSenergy savingssimulation
Type
Research Article
Information
AI EDAM , Volume 33 , Special Issue 2: Knowledge Engineering and Management Applied to Innovation , May 2019 , pp. 138 - 147
Copyright
Copyright © Cambridge University Press 2019 

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