Abstract

Research Article

Investigation of snow load reduction in the industrial sheds roof design with photovoltaic systems by mathematical modelling, solar system evaluation, X-steel simulation and thermodynamic practices

Nasim Ghadami, Bita Deravian, Behzad Deravian, Amir Takhtravan, Seyed Mohammad Khatibi and Mohammad Gheibi*

Published: 08 September, 2021 | Volume 5 - Issue 1 | Pages: 011-016

Since snow load is one of the loads of designing the industrial shed roof, this research presents a new system to reduce the industrial sheds roof design. In this system, sensitive units of moisture and temperature, which can be adjusted with different areas, are installed on the shed’s roof. The mechanism of system is that the sensors in the units detect the presence of snow on the shed roof and send an order to connect electricity to the elements; therefore, the snow on the roof melts by the heat generated. In this system, solar panels are used to supply electricity. As with the help of this mechanism, snow does not remain on the roof, it is possible to eliminate the snow load in the calculations of the shed and apply at least the live load of the sixth regulation (Due to having a one-story shed, minimum live load applied and it used only for the foundation design of the structure.), this issue will create an economic plan in shed designing. According to the study conducted in this research, it is shown that the dimensions of the sheet beam used in the shed are reduced, which will significantly reduce the cost of construction and installation to some extent. In the following, two samples of sheds with a span of 20 meters in the presence of snow and the absence of snow in the software were modelled, and the results were compared with each other.

Read Full Article HTML DOI: 10.29328/journal.acee.1001030 Cite this Article Read Full Article PDF

Keywords:

Industrial shed; Solar panels; Photovoltaic system; Thermodynamic evaluation; Green energy supplying

References

  1. Ghadami N, Gheibi M, Kian Z, Faramarz MG, Naghedi R, et al. Implementation of solar energy in smart cities using an integration of artificial neural network, photovoltaic system and classical Delphi methods. Sustaina Cities Soc. 2021; 74: 103149.
  2. Ghadami N, Siamaki M, Pouresmaeil H, Aghlmand R, Gheibi M. Assessing Energy Consumption, Optical Distributions, and Carbon Contaminations using the Design-Builder Simulation Model (Case Study: A Sports Building, Mashhad, Iran). Ann Environ Sci Toxicol. 2021; 5: 074-079. https://www.peertechzpublications.com/articles/AEST-5-140.php
  3. Bahramian F, Akbari A, Nabavi M, Esfandi S, Naeiji E. Design and tri-objective optimization of an energy plant integrated with near-zero energy building including energy storage: An application of dynamic simulation. Sustaina Energy Technolog Assess. 2021; 47: 101419.
  4. Taherahmadi J, Noorollahi Y, Panahi M. Toward comprehensive zero energy building definitions: a literature review and recommendations. Int J Sustaina Energy. 2021; 40: 120-148.
  5. Rabani M, Madessa HB, Nord N. Achieving zero-energy building performance with thermal and visual comfort enhancement through optimization of fenestration, envelope, shading device, and energy supply system. Sustaina Energy Technolog Assess. 2021; 44, 101020.
  6. Abdou N, Mghouchi YE, Hamdaoui S, Asri NE, Mouqallid M. Multi-objective optimization of passive energy efficiency measures for net-zero energy building in Morocco. Building Environ. 2021; 204: 108141.
  7. Guo J, Dong J, Wang H, Jiang Y, Tao J. On-site measurement of the thermal performance of a novel ventilated thermal storage heating floor in a nearly zero energy building. Building Environ. 2021; 107993.
  8. De Luca G, Ballarini I, Paragamyan A, Pellegrino A, Corrado V. On the improvement of indoor environmental quality, energy performance and costs for a commercial nearly zero-energy building. Sci Technol Built Environ. 2021; 1-19.
  9. Bal M, Stok FM, Van Hemel C, De Wit JB. Including Social Housing Residents in the Energy Transition: A Mixed-Method Case Study on Residents' Beliefs, Attitudes, and Motivation Toward Sustainable Energy Use in a Zero-Energy Building Renovation in the Netherlands. Front Sustaina Cities. 2021; 3, 25.
  10. Migliori Favaretto M. Modeling and Energy Simulation of a Zero Energy Building: A Case Study for Florida(Doctoral dissertation).
  11. Khassan А, Donenko VI, Ischenko OL. The use of BIM to achieve zero energy building. Metal Sci Heat Treat Metals. 2021; 1: 59-65.
  12. Woo J, Fatima R, Kibert CJ, Newman RE, Tian Y, et al. Applying blockchain technology for building energy performance measurement, reporting, and verification (MRV) and the carbon credit market: A review of the literature. Building Environ. 2021; 108199.
  13. Guo S, Yan D, Hu S, Zhang Y. Modelling building energy consumption in China under different future scenarios.  2021; 214: 119063.
  14. Ahmadi MM, Keyhani A, Kalogirou SA, Lam SS, Peng W, et al. Net-zero exergoeconomic and exergoenvironmental building as new concepts for developing sustainable built environments. Energy Convers Manage. 2021; 244: 114418.
  15. Rimec D. Multidimensional Assessment For a Case Studied Zero Energy Building: Climate positive buildings with and without a connection to the district heating network. 2021;
  16. Arabkoohsar A, Behzadi A, Alsagri AS. Techno-economic analysis and multi-objective optimization of a novel solar-based building energy system; An effort to reach the true meaning of zero-energy buildings. Energy Convers Manage. 2021; 232: 113858.
  17. Yu L, Qin S, Zhang M, Shen C, Jiang T, et al. A review of deep reinforcement learning for smart building energy management. IEEE Internet Things J.
  18. Li L, Sun W, Hu W, Sun Y. Impact of natural and social environmental factors on building energy consumption: Based on bibliometrics. J Building Eng. 2021; 102136.
  19. Kathirgamanathan A, De Rosa M, Mangina E, Finn DP. Data-driven predictive control for unlocking building energy flexibility: A review. Renewa Sustaina Energy Rev. 2021; 135: 110120.
  20. Chegari B, Tabaa M, Simeu E, Moutaouakkil F, Medromi H. Multi-objective optimization of building energy performance and indoor thermal comfort by combining artificial neural networks and metaheuristic algorithms. Energy Buildings. 2021; 239: 110839.
  21. Verhaeghe C, Verbeke S, Audenaert A. A consistent taxonomic framework: towards common understanding of high energy performance building definitions. Renewa Sustaina Energy Rev. 2021; 146: 111075.
  22. Deb C, Schlueter A. Review of data-driven energy modelling techniques for building retrofit. Renewable and Sustainable Energy Reviews. 2021; 144: 110990.
  23. Wang L, Lee EW, Hussian SA, Yuen ACY, Feng W. Quantitative impact analysis of driving factors on annual residential building energy end-use combining machine learning and stochastic methods. Applied Energy. 2021; 299: 117303.
  24. Luo XJ, Oyedele LO. Forecasting building energy consumption: Adaptive long-short term memory neural networks driven by genetic algorithm. Adv Eng Informat. 2021; 50: 101357.
  25. Hong Y, Yoon S, Kim YS, Jang H. System-level virtual sensing method in building energy systems using autoencoder: Under the limited sensors and operational datasets. Applied Energy. 2021; 301: 117458.
  26. Langevin J, Harris CB, Satre-Meloy A, Chandra-Putra H, Speake A, et al. US building energy efficiency and flexibility as an electric grid resource.  2021;
  27. Tian Z, Shi X, Hong SM. Exploring data-driven building energy-efficient design of envelopes based on their quantified impacts. J Building Eng. 2021; 42: 103018.
  28. Ghalambaz M, Yengejeh RJ, Davami AH. Building energy optimization using Grey Wolf Optimizer (GWO). Case Studies Thermal Eng. 2021; 27: 101250.
  29. Veiga RK, Veloso AC, Melo AP, Lamberts R. Application of machine learning to estimate building energy use intensities. Energy Buildings. 2021; 249: 111219.
  30. Liu B, Rodriguez D. Renewable energy systems optimization by a new multi-objective optimization technique: A residential building. J Building Eng. 2021; 35: 102094.
  31. Rodriguez A, Smith ST, Potter B. Sensitivity analysis for building energy audit calculation methods: Handling the uncertainties in small power load estimation.  2021; 238: 121511.
  32. Salom J, Tamm M, Andresen I, Cali D, Magyari Á, et al. An Evaluation Framework for Sustainable Plus Energy Neighbourhoods: Moving Beyond the Traditional Building Energy Assessment. Energies. 2021; 14: 4314.
  33. Yu H, Wang M, Lin X, Guo H, Liu H, et al. Prioritizing urban planning factors on community energy performance based on GIS-informed building energy modeling. Energy Buildings. 2021; 249: 111191.
  34. Bastos Porsani G, Del Valle de Lersundi K, Sánchez-Ostiz Gutiérrez A, Fernández Bandera C. Interoperability between Building Information Modelling (BIM) and Building Energy Model (BEM). Appl Sci. 2021; 11: 2167.
  35. Gulotta TM, Cellura M, Guarino F, Longo A bottom-up harmonized energy-environmental models for europe (BOHEEME): A case study on the thermal insulation of the EU-28 building stock. Energy Buildings. 2021; 231: 110584.

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