Performance enhancement and life cycle analysis of a novel solar HVAC system using underground water and energy recovery technique

Rahman, S. M. A.; Issa, Salah; Assad, Mamdouh El Haj; Shah, Sheikh Khaleduzzaman; Abdelkareem, Mohammad Ali; Hoque, Md Enamul; Olabi, Abdul Ghani

doi:10.1016/j.tsep.2022.101515

Cited by 4 publications

(3 citation statements)

References 44 publications

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“…Computational resources: the computational complexity and resource requirements of deep learning and reinforcement learning models can pose practical challenges for their integration into architectural design workflows, potentially requiring specialized hardware and computational resources [55,68,90];…”

Section: Challenges and Limitationsmentioning

confidence: 99%

“…Deep learning, a powerful machine learning technique inspired by the human brain's neural networks, excels at modeling intricate patterns and relationships from large datasets [44][45][46][47][48][49][50]. On the other hand, reinforcement learning enables software agents to learn optimal decision-making strategies through trial-and-error interactions with an environment, guided by reward signals [51][52][53][54][55][56].…”

Section: Introductionmentioning

confidence: 99%

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Harnessing Deep Learning and Reinforcement Learning Synergy as a Form of Strategic Energy Optimization in Architectural Design: A Case Study in Famagusta, North Cyprus

Karimi,

Adibhesami,

Hoseinzadeh

et al. 2024

Buildings

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This study introduces a novel framework that leverages artificial intelligence (AI), specifically deep learning and reinforcement learning, to enhance energy efficiency in architectural design. The goal is to identify architectural arrangements that maximize energy efficiency. The complexity of these models is acknowledged, and an in-depth analysis of model selection, their inherent complexity, and the hyperparameters that govern their operation is conducted. This study validates the scalability of these models by comparing them with traditional optimization techniques like genetic algorithms and simulated annealing. The proposed system exhibits superior scalability, adaptability, and computational efficiency. This research study also explores the ethical and societal implications of integrating AI with architectural design, including potential impacts on human creativity, public welfare, and personal privacy. This study acknowledges it is in its preliminary stage and identifies its potential limitations, setting the stage for future research to enhance and expand the effectiveness of the proposed methodology. The findings indicate that the model can steer the architectural field towards sustainability, with a demonstrated reduction in energy usage of up to 20%. This study also conducts a thorough analysis of the ethical implications of AI in architecture, emphasizing the balance between technological advancement and human creativity. In summary, this research study presents a groundbreaking approach to energy-efficient architectural design using AI, with promising results and wide-ranging applicability. It also thoughtfully addresses the ethical considerations and potential societal impacts of this technological integration.

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Section: Challenges and Limitationsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Harnessing Deep Learning and Reinforcement Learning Synergy as a Form of Strategic Energy Optimization in Architectural Design: A Case Study in Famagusta, North Cyprus

Karimi,

Adibhesami,

Hoseinzadeh

et al. 2024

Buildings

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show abstract

“…A novel thermoelectric-energy recovery system was shown by Ramadan et al [24], highlighting the potential of thermoelectric generators (TEGs) in the generation of green energy. Additional studies looked at solar-assisted HVAC systems [25] and HVAC system design layout optimization [26], providing information about cutting-edge methods and their drawbacks. Furthermore, it has been noted that centralized model predictive control (MPC) techniques have promise for managing comfort and energy in homes powered by solar energy [27].…”

Section: Introductionmentioning

confidence: 99%

Geothermal energy concept applied to All-Air HVAC system

Darwiche,

Rabih,

Faraj

et al. 2024

J. Phys.: Conf. Ser.

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The novel method of combining geothermal energy with an all-air heating, ventilation, and air conditioning (HVAC) system is presented in this work. A building in Lebanon serves as the case study, which aims to determine the necessary mass flow rate for the conditioned space. Rather than permitting ambient air to enter the Air Handling Unit (AHU) directly, the plan calls for a 2-meter-deep geothermal duct to be buried. By adjusting the air temperature prior to it entering the AHU, this geothermal duct helps to improve the efficiency of the HVAC system and lowers the AHU’s yearly energy usage. Furthermore, the idea guarantees that there will always be 100% fresh air available, which means that the all-air HVAC system won’t need to recycle treated air from the conditioned space—also known as return air. According to the findings, the heat rate of the geothermal duct may reach 210 kW, which would result in a large decrease in CO2 emissions and cost savings for the HVAC system.

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Geothermal systems classification, coupling, and hybridization: A recent comprehensive review

Dokmak,

Faraj,

Faraj

et al. 2024

Energy and Built Environment

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Performance enhancement and life cycle analysis of a novel solar HVAC system using underground water and energy recovery technique

Cited by 4 publications

References 44 publications

Harnessing Deep Learning and Reinforcement Learning Synergy as a Form of Strategic Energy Optimization in Architectural Design: A Case Study in Famagusta, North Cyprus

Harnessing Deep Learning and Reinforcement Learning Synergy as a Form of Strategic Energy Optimization in Architectural Design: A Case Study in Famagusta, North Cyprus

Geothermal energy concept applied to All-Air HVAC system

Geothermal systems classification, coupling, and hybridization: A recent comprehensive review

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