Polyethylene Bubble Aluminium SB250-FR+ for Reduced Energy Consumption Building: An Experimental Study

Sousa, Mateus De; Muhieldeen, Mohammed W.; Tey, Wah Yen; Hou, Teng Kah; Rahman, U. Z. A.

doi:10.1007/978-981-19-1457-7_52

Cited by 1 publication

(1 citation statement)

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“…For instance, in Morocco, it reached as high as 33% [2]. Improving a building's energy efficiency involves a number of approaches, such as insulation, upgraded window glazing, and economizer controls, which can significantly reduce energy consumption [3][4][5][6][7][8][9]. Geographical location significantly influences the energy demands of HVAC systems, underlining the importance of global considerations in building design [9], and HVAC systems alone may account for more than 60% of a building's energy use [10].…”

Section: Introductionmentioning

confidence: 99%

Advancing Sustainable Building Practices: Intelligent Methods for Enhancing Heating and Cooling Energy Efficiency

Agouzoul,

Simeu,

Tabaa

2024

Sustainability

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Our work is dedicated to enhancing sustainability through improved energy efficiency in buildings, with a specific focus on heating and cooling control and the optimization of thermal comfort of occupants. With an energy consumption of more than 60% in buildings, HVAC systems are the biggest energy users. By integrating advanced technology, data algorithms, and digital twins, our study aims to optimize energy performance effectively. We have developed a Neural Network-based Model Predictive Control (NNMPC) to achieve this goal. Leveraging technologies such as MQTT communication, Wi-Fi modules, and field-programmable gate arrays will enhance scalability and flexibility. Our findings demonstrate the efficacy of the NNMPC system deployed on the PYNQ board for reducing sensible thermal energy usage for both cooling and heating purposes. Compared to traditional On/Off control systems, the NNMPC achieved an impressive 40.8% reduction in heating energy consumption and a 37.8% decrease in cooling energy consumption in 2006. In comparison to the On/Off technique, the NNMPC demonstrated a 25.6% reduction in annual heating energy consumption and a 28.8% drop in annual cooling energy consumption in the simulated year of 2017. We observed that, across all strategies and platforms, there were no instances where the Predicted Mean Vote (PMV) fell below −0.5. However, a significant proportion of PMV values (ranging from 65% to 83%) were observed between −0.5 and 0.5, signifying a high level of occupant comfort. Additionally, for PMV values between 0.5 and 1.0, percentages ranged from 16% to 33% for both years. Importantly, the NNMPC exhibited notable efficiency in maintaining occupants’ comfort within this range, requiring less energy while ensuring highly satisfactory environments.

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