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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*
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: [email protected]

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.

Type
Research Article
Copyright
Copyright © Cambridge University Press 2019 

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References

Aydinalp, M, Ismet Ugursal, V and Fung, AS (2004) Modeling of the space and domestic hot-water heating energy-consumption in the residential sector using neural networks. Applied Energy 79, 159178.Google Scholar
Bacher, P, Madsen, H, Nielsen, HA and Perers, B (2013) Short-term heat load forecasting for single family houses. Energy and Buildings 65, 101112.Google Scholar
Bales, C, Betak, J, Broum, M, Chèze, D, Cuvillier, G, Haberl, R, Hafner, B, Haller, MY, Hamp, Q, Heinz, A, Hengel, F, Kruck, A, Matuska, T, Mojic, I, Petrak, J, Poppi, S, Sedlar, J, Sourek, B, Thissen, B, Weidinger, A (2015). Optimized solar and heat pump systems, components and dimensioning. Available at http://www.macsheep.spf.ch/fileadmin/user_upload/macsheep/dokumente/MacSheep_D7-3_Simulations_M36_v150618_Final_revised.pdfGoogle Scholar
Biaou, AL and Bernier, MA (2008) Achieving total domestic hot water production with renewable energy. Building and Environment 43, 651660.Google Scholar
Box, GEP, Jenkins, GM and Reinsel, GC (2013) Time Series Analysis: Forecasting and Control. Hoboken, New Jersey: John Wiley & Sons.Google Scholar
Chèze, D, Bales, C, Betak, J, Broum, M, Heier, J, Heinz, A and Poppi, S (2015) Final report on Control strategies, fault detection and on-line diagnosis in WP6-Deliverable 6.4: MacSheep-New Materials and Control for a next generation of compact combined Solar and heat pump systems with boosted energetic and exergetic performance.Google Scholar
Deb, C, Zhang, F, Yang, J, Lee, SE and Shah, KW (2017) A review on time series forecasting techniques for building energy consumption. Renewable and Sustainable Energy Reviews 74, 902924.Google Scholar
Eslami-nejad, P and Bernier, M (2009) Impact of grey water heat recovery on the electrical demand of domestic hot water heaters. In 11th IBPSA Conference, Glasgow.Google Scholar
Eynard, J, Grieu, S and Polit, M (2012) Predictive control and thermal energy storage for optimizing a multi-energy district boiler. Journal of Process Control 22, 12461255.Google Scholar
Gelažanskas, L and Gamage, KAA (2015) Forecasting hot water consumption in dwellings using artificial neural networks. In Power Engineering, Energy and Electrical Drives (POWERENG), 2015 IEEE 5th International Conference on (pp. 410–415).Google Scholar
Haller, M, Dott, R, Ruschenburg, J, Ochs, F and Bony, J (2013) The Reference Framework for System Simulations of the IEA SHC Task 44/HPP Annex 38 Part A: General Simulation boundary conditions. International Energy Agency, A Technical Report of Subtask C Report C1 Part A.Google Scholar
Halvgaard, R, Bacher, P, Perers, B, Andersen, E, Furbo, S, Jørgensen, JB, Poulsen, NK, Madsen, H (2012) Model predictive control for a smart solar tank based on weather and consumption forecasts. Energy Procedia 30, 270278.Google Scholar
Jordan, U and Vajen, K (2001) Influence of the DHW load profile on the fractional energy savings: a case study of a solar combi-system with TRNSYS simulations. Solar Energy 69, 197208.Google Scholar
Lomet, A, Suard, F and Chèze, D (2015) Statistical modeling for real domestic hot water consumption forecasting. Energy Procedia 70, 379387.Google Scholar
Lomet, A, Denis, Y, Suard, F and Chèze, D (2015). Statistical modeling to forecast the domestic hot water consumption. Energy and Building 70, 379387.Google Scholar
NégaWatt (2016) Projet “Europe-Territoires”: Transition(s) énergétique(s) en Europe: analyse comparative de scénario, de leur application territoriale et de leurs impacts socio-économiques. Retrieved from https://negawatt.org/IMG/pdf/160908_rapport_transitionsenergetiques_europe.pdfGoogle Scholar
Nielsen, HA and Madsen, H (2006) Modelling the heat consumption in district heating systems using a grey-box approach. Energy and Buildings 38, 6371.Google Scholar
Popescu, D and Serban, E (2008) Simulation of domestic hot-water consumption using time-series models. In Proceedings of the 6th IASME/WSEAS International Conference on Heat Transfer, Thermal Engineering and Environment, Rhodes, Greece (pp. 20–22).Google Scholar
Prud homme, T and Gillet, D (2001) Advanced control strategy of a solar domestic hot water system with a segmented auxiliary heater. Energy and Buildings 33, 463475.Google Scholar
Rousseeuw, PJ (1987) Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics 20, 5365.Google Scholar
Sandels, C, Widén, J and Nordström, L (2014) Forecasting household consumer electricity load profiles with a combined physical and behavioral approach. Applied Energy 131, 267278.Google Scholar
Sossan, F, Kosek, AM, Martinenas, S, Marinelli, M and Bindner, H (2013) Scheduling of domestic water heater power demand for maximizing PV self-consumption using model predictive control. In Innovative Smart Grid Technologies Europe (ISGT EUROPE), 2013 4th IEEE/PES (pp. 1–5).Google Scholar
Spur, R, Fiala, D, Nevrala, D and Probert, D (2006) Influence of the domestic hot-water daily draw-off profile on the performance of a hot-water store. Applied Energy 83, 749773.Google Scholar
Sterling, SJ and Collins, MR (2012) Feasibility analysis of an indirect heat pump assisted solar domestic hot water system. Applied Energy 93, 1117.Google Scholar
Suganthi, L and Samuel, AA (2012) Energy models for demand forecasting – a review. Renewable and Sustainable Energy Reviews 16, 12231240.Google Scholar
Swan, LG, Ugursal, VI and Beausoleil-Morrison, I (2011) Occupant related household energy consumption in Canada: estimation using a bottom-up neural-network technique. Energy and Buildings 43, 326337.Google Scholar
Xi, C, Lin, L and Hongxing, Y (2011) Long term operation of a solar assisted ground coupled heat pump system for space heating and domestic hot water. Energy and Buildings 43, 18351844.Google Scholar