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

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 presented previously 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 carried on using TRNSYS software to calculate the total energy consumption of DHW production systems over one 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) and the forecast provided by our algorithm. The measurements and simulations are carried 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 system and anticipation could be evaluated. I. Introduction A recent study [1] showed that global energy consumption should be halved for European countries to meet 2050 ambitious objective of 75 to 90 % reduction of greenhouse gases emissions. Energy saving is thus a major component of the effort that should be carried on for our future, along with the development of renewable energies. Considering more precisely the residential sector, Space Heating (SH) and Domestic Hot Water (DHW) production represent a significant part of the total energy consumption , depending on the country and the geographical location. For example, heating, air conditioning and hot water production represent up to 65 and 77 % of the total
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Contributor : David Chèze <>
Submitted on : Tuesday, May 12, 2020 - 6:01:10 PM
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Yvan Denis, Frédéric Suard, Aurore Lomet, David Chèze. Saving energy by anticipating hot water production: identification of key points for an efficient statistical model integration. AI EDAM, Cambridge University Press (CUP), 2019, 33 (02), pp.138-147. ⟨10.1017/S0890060419000143⟩. ⟨cea-02571139⟩



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