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Analysis of energy demand in a residential building using TRNSYS

Author

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  • Rashad, Magdi
  • Żabnieńska-Góra, Alina
  • Norman, Les
  • Jouhara, Hussam

Abstract

Energy simulations of buildings complement or replace the static calculations used so far and provide detailed answers to questions such as: what is the energy demand for individual purposes in a building and how does it change over the course of a day, a month, a year and also enable a comparison of several design variants and the selection of the optimal one in terms of energy consumption. Therefore, energy simulations of buildings help make decisions to optimise architectural and installation solutions, leading to a reduction in electricity, gas and water demand for the designed building. They indicate to what extent individual factors affect the demand for heating, cooling and electricity. The results of the analyses can be used as a basis for design and system decisions, and also provide interesting feedback to the investor.

Suggested Citation

  • Rashad, Magdi & Żabnieńska-Góra, Alina & Norman, Les & Jouhara, Hussam, 2022. "Analysis of energy demand in a residential building using TRNSYS," Energy, Elsevier, vol. 254(PB).
    • Handle: RePEc:eee:energy:v:254:y:2022:i:pb:s0360544222012609
    DOI: 10.1016/j.energy.2022.124357
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    1. Battaglia, Vittoria & Vanoli, Laura & Verde, Clara & Nithiarasu, Perumal & Searle, Justin R., 2023. "Dynamic modelling of geothermal heat pump system coupled with positive-energy building," Energy, Elsevier, vol. 284(C).
    2. Kotarela, Faidra & Kyritsis, Anastasios & Agathokleous, Rafaela & Papanikolaou, Nick, 2023. "On the exploitation of dynamic simulations for the design of buildings energy systems," Energy, Elsevier, vol. 271(C).
    3. Ali Yildirim, Mehmet & Bartyzel, Filip & Vallati, Andrea & Woźniak, Magdalena Kozień & Ocłoń, Paweł, 2023. "Efficient energy storage in residential buildings integrated with RESHeat system," Applied Energy, Elsevier, vol. 335(C).
    4. Lee, Tae-Kyu & Kim, Jeong-Uk, 2024. "Two processes based on a data-driven model combined with dynamic simulation for demand forecasting and providing energy saving measures," Energy, Elsevier, vol. 300(C).
    5. Xiaolei Yuan & Mingya Zhu & Yumin Liang & Mehdi Shahrestani & Risto Kosonen, 2023. "Comparison of Short and Long-Term Energy Performance and Decarbonization Potentials between Cogeneration and GSHP Systems under MARKAL Scenarios," Sustainability, MDPI, vol. 15(2), pages 1-23, January.
    6. Luigi Maffei & Antonio Ciervo & Achille Perrotta & Massimiliano Masullo & Antonio Rosato, 2023. "Innovative Energy-Efficient Prefabricated Movable Buildings for Smart/Co-Working: Performance Assessment upon Varying Building Configurations," Sustainability, MDPI, vol. 15(12), pages 1-37, June.

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