A proactive energy-efficient optimal ventilation system using artificial intelligent techniques under outdoor air quality conditions

Nam, Ki Jeon; Heo, SungKu; Li, Qian; Loy-Benitez, Jorge; Kim, Min Jeong; Park, Duck Shin

doi:10.1016/j.apenergy.2020.114893

Cited by 44 publications

(8 citation statements)

References 40 publications

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“…Table 7 presents the studies on the application of AI for HVAC controls, the models and the building types. Again, in 2020, Nam et al [109] developed AI-based models to improve the efficiency of the ventilation system in a subway station. Using a DL and artificial intelligence iterative dynamic programming (AI-IDP) combination, the DL was used to predict the weather conditions of the influencing outdoors for 24 h ahead, while the AI-IDP was used to optimize the operations of the ventilation system for the same predicted period.…”

Section: For Hvac Controlsmentioning

confidence: 99%

A Systematic Review on the Use of AI for Energy Efficiency and Indoor Environmental Quality in Buildings

Ogundiran,

Asadi,

Gameiro da Silva

2024

Sustainability

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Global warming, climate change and the energy crisis are trending topics around the world, especially within the energy sector. The rising cost of energy, greenhouse gas (GHG) emissions and global temperatures stem from the over-reliance on fossil fuel as the major energy resource. These challenges have highlighted the need for alternative energy resources and urgent intervention strategies like energy consumption reduction and improving energy efficiency. The heating, ventilation, and air-conditioning (HVAC) system in a building accounts for about 70% of energy consumption, and a decision to reduce energy consumption may impact the indoor environmental quality (IEQ) of the building. It is important to adequately balance the tradeoff between IEQ and energy management. Artificial intelligence (AI)-based solutions are being explored for improving building energy performance without compromising IEQ. This paper systematically reviews recent studies on AI and machine learning (ML) for building energy management and IEQ by exploring common use areas, the methods or algorithms applied and the results obtained. The overall purpose of this research is to add to the existing body of work and to highlight energy-related AI applications in buildings and the related gaps. The result shows five common application areas: thermal comfort and indoor air quality (IAQ) control; energy management and energy consumption prediction; indoor temperature prediction; anomaly detection; and HVAC controls. Gaps involving policy, real-life scenario applications, and insufficient study of the visual and acoustic comfort areas are also identified. Very few studies take into consideration the need to follow IEQ standards in the selection process and positioning of sensors in AI applications for IEQ in buildings. This study reveals a need for more systematically summarized research.

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Section: For Hvac Controlsmentioning

confidence: 99%

A Systematic Review on the Use of AI for Energy Efficiency and Indoor Environmental Quality in Buildings

Ogundiran,

Asadi,

Gameiro da Silva

2024

Sustainability

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“…The strategy was capable of identifying windows' status with an average accuracy of 97.29%, indicating its potential to help building managers to prevent unnecessary heating or cooling demand. Nam et al [62] developed an energy-efficient management system for underground ventilation based on AI-iterative dynamic programming technology. The energy efficiency could be improved by almost 8.68% after maintaining the subway's indoor air quality, equaling a decrease of 96 t/y of CO 2 .…”

Section: Energy Saving and Emission Reductionmentioning

confidence: 99%

Recent Advances in Low-Carbon and Sustainable, Efficient Technology: Strategies and Applications

et al. 2022

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The COVID-19 pandemic has had a significant impact on the supply chains of traditional fossil fuels. According to a report by the International Energy Agency (IEA) from 2020, oil-refining activity fell by more than the IEA had anticipated. It was also assumed that the demand in 2021 would likely be 2.6 million bpd below the 2019 levels. However, renewable markets have shown strong resilience during the crisis. It was determined that renewables are on track to meet 80% of the growth in electricity demand over the next 10 years and that sustainable energy will act as the primary source of electricity production instead of coal. On the other hand, the report also emphasized that measures for reducing environmental pollution and CO2 emissions are still insufficient and that significant current investments should be further expanded. The Sustainable Development of Energy, Water and Environment Systems (SDEWES) conference series is dedicated to the advancement and dissemination of knowledge on methods, policies and technologies for improving the sustainability of development by decoupling growth from the use of natural resources. The 15th SDEWES conference was held online from 1–5 September 2020; more than 300 reports with 7 special sections were organized on the virtual conference platform. This paper presents the major achievements of the recommended papers in the Special Issue of Energies. Additionally, related studies connected to the above papers published in the SDEWES series are also introduced, including the four main research fields of energy saving and emission reduction, renewable energy applications, the development of district heating systems, and the economic assessment of sustainable energy.

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“…Other control strategies, such as adaptive control and reinforcement learning control, [82,83] have also been applied in existing studies. Wang et al [84] retrofitted HVAC control systems in UMSs based on distributed control architecture with centralized management.…”

Section: Other Control Strategiesmentioning

confidence: 99%

A review on available energy saving strategies for heating, ventilation and air conditioning in underground metro stations

You

Zhang

et al. 2021

Renewable and Sustainable Energy Reviews

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A proactive energy-efficient optimal ventilation system using artificial intelligent techniques under outdoor air quality conditions

Cited by 44 publications

References 40 publications

A Systematic Review on the Use of AI for Energy Efficiency and Indoor Environmental Quality in Buildings

A Systematic Review on the Use of AI for Energy Efficiency and Indoor Environmental Quality in Buildings

Recent Advances in Low-Carbon and Sustainable, Efficient Technology: Strategies and Applications

A review on available energy saving strategies for heating, ventilation and air conditioning in underground metro stations

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