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A New System to Estimate and Reduce Electrical Energy Consumption of Domestic Hot Water in Spain

Author

Listed:
  • Alberto Gutierrez-Escolar

    (Department of Computer Sciences, Polytechnic School, University of Alcala, Road Madrid-Barcelona, Km 33.6, Alcala de Henares 28871, Spain)

  • Ana Castillo-Martinez

    (Department of Computer Sciences, Polytechnic School, University of Alcala, Road Madrid-Barcelona, Km 33.6, Alcala de Henares 28871, Spain)

  • Jose M. Gomez-Pulido

    (Department of Computer Sciences, Polytechnic School, University of Alcala, Road Madrid-Barcelona, Km 33.6, Alcala de Henares 28871, Spain)

  • Jose-Maria Gutierrez-Martinez

    (Department of Computer Sciences, Polytechnic School, University of Alcala, Road Madrid-Barcelona, Km 33.6, Alcala de Henares 28871, Spain)

  • Zlatko Stapic

    (Faculty of Organization and Informatics, University of Zagreb, Pavlinska 2, Varazdin 42000, Croatia)

Abstract

Energy consumption rose about 28% over the 2001 to 2011 period in the Spanish residential sector. In this environment, domestic hot water (DHW) represents the second highest energy demand. There are several methodologies to estimate DHW consumption, but each methodology uses different inputs and some of them are based on obsolete data. DHW energy consumption estimation is a key tool to plan modifications that could enhance this consumption and we decided to update the methodologies. We studied DHW consumption with data from 10 apartments in the same building during 18 months. As a result of the study, we updated one chosen methodology, adapting it to the current situation. One of the challenges to improve efficiency of DHW use is that most of people are not aware of how it is consumed in their homes. To help this information to reach consumers, we developed a website to allow users to estimate the final electrical energy needed for DHW. The site uses three estimation methodologies and chooses the best fit based on information given by the users. Finally, the application provides users with recommendations and tips to reduce their DHW consumption while still maintaining the desired comfort level.

Suggested Citation

  • Alberto Gutierrez-Escolar & Ana Castillo-Martinez & Jose M. Gomez-Pulido & Jose-Maria Gutierrez-Martinez & Zlatko Stapic, 2014. "A New System to Estimate and Reduce Electrical Energy Consumption of Domestic Hot Water in Spain," Energies, MDPI, vol. 7(11), pages 1-19, October.
    • Handle: RePEc:gam:jeners:v:7:y:2014:i:11:p:6837-6855:d:41630
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    References listed on IDEAS

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    1. Rodríguez-Hidalgo, M.C. & Rodríguez-Aumente, P.A. & Lecuona, A. & Legrand, M. & Ventas, R., 2012. "Domestic hot water consumption vs. solar thermal energy storage: The optimum size of the storage tank," Applied Energy, Elsevier, vol. 97(C), pages 897-906.
    2. Luis R. Bernardo & Henrik Davidsson & Björn Karlsson, 2012. "Retrofitting Domestic Hot Water Heaters for Solar Water Heating Systems in Single-Family Houses in a Cold Climate: A Theoretical Analysis," Energies, MDPI, vol. 5(10), pages 1-22, October.
    3. Izquierdo, M. & Moreno-Rodríguez, A. & González-Gil, A. & García-Hernando, N., 2011. "Air conditioning in the region of Madrid, Spain: An approach to electricity consumption, economics and CO2 emissions," Energy, Elsevier, vol. 36(3), pages 1630-1639.
    4. Aydinalp, Merih & Ismet Ugursal, V. & Fung, Alan S., 2004. "Modeling of the space and domestic hot-water heating energy-consumption in the residential sector using neural networks," Applied Energy, Elsevier, vol. 79(2), pages 159-178, October.
    5. Larsen, Bodil Merethe & Nesbakken, Runa, 2004. "Household electricity end-use consumption: results from econometric and engineering models," Energy Economics, Elsevier, vol. 26(2), pages 179-200, March.
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    Cited by:

    1. Katarzyna Ratajczak & Katarzyna Michalak & Michał Narojczyk & Łukasz Amanowicz, 2021. "Real Domestic Hot Water Consumption in Residential Buildings and Its Impact on Buildings’ Energy Performance—Case Study in Poland," Energies, MDPI, vol. 14(16), pages 1-22, August.
    2. Hyerim Yoon & David Sauri & Antonio M. Rico Amorós, 2018. "Shifting Scarcities? The Energy Intensity of Water Supply Alternatives in the Mass Tourist Resort of Benidorm, Spain," Sustainability, MDPI, vol. 10(3), pages 1-17, March.
    3. Wojciech Rzeźnik & Ilona Rzeźnik & Paweł Hara, 2022. "Comparison of Real and Forecasted Domestic Hot Water Consumption and Demand for Heat Power in Multifamily Buildings, in Poland," Energies, MDPI, vol. 15(19), pages 1-17, September.

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