Saving energy by anticipating hot water production: identification of key points for an efficient statistical model integration. Issue 2 (6th May 2019)

• Record Type: Journal Article
• Authors: Denis, Yvan; Suard, Frédéric; Lomet, Aurore; Chèze, David
• Editors: Matta, Nada; Mercier-Laurent, Eunika; Carrillo, Tatiana Reyes; Boulanger, Danielle
• Is Part Of: AI EDAM. Volume 33:Issue 2(2019)
• Page Start: 138
• Page End: 147
• Publication Date: 2019-05-06
• Subjects: ARIMA, -- consumption forecast, -- ECS, -- energy savings, -- simulation; Engineering design -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; 620.00420285
• DOI: 10.1017/S0890060419000143
• Languages: English
• ISSNs: 0890-0604

Abstract

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.