An application of Bayesian Network approach for selecting energy efficient HVAC systems

Tian, Zhichao; Si, Binghui; Shi, Xing; Fang, Zhong-Liao

doi:10.1016/j.jobe.2019.100796

Cited by 36 publications

(18 citation statements)

References 27 publications

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“…Using thermally efficient materials to improve HVAC, such as radiant ceilings and thermally active building systems tested by Gärtner et al [ 30 ] for flexible office spaces in Stuttgart (Germany), is another solution. Finally, applying methods, such as the Bayesian network technique to select the most energy-efficient primary HVAC systems, should also be considered [ 31 ]. Furthermore, according to the reference [ 32 ], another promising strategy for providing energy savings in buildings is the use of energy recovery systems.…”

Section: Introductionmentioning

confidence: 99%

A State of the Art of the Overall Energy Efficiency of Wood Buildings—An Overview and Future Possibilities

Cabral

Blanchet

2021

Materials

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The main goal of this study was to review current studies on the state of the art of wood constructions with a particular focus on energy efficiency, which could serve as a valuable source of information for both industry and scholars. This review begins with an overview of the role of materials in wood buildings to improve energy performance, covering structural and insulation materials that have already been successfully used in the market for general applications over the years. Subsequently, studies of different wood building systems (i.e., wood-frame, post-and-beam, mass timber and hybrid constructions) and energy efficiency are discussed. This is followed by a brief introduction to strategies to increase the energy efficiency of constructions. Finally, remarks and future research opportunities for wood buildings are highlighted. Some general recommendations for developing more energy-efficient wood buildings are identified in the literature and discussed. There is a lack of emerging construction concepts for wood-frame and post-and-beam buildings and a lack of design codes and specifications for mass timber and hybrid buildings. From the perspective of the potential environmental benefits of these systems as a whole, and their effects on energy efficiency and embodied energy in constructions, there are barriers that need to be considered in the future.

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Section: Introductionmentioning

confidence: 99%

A State of the Art of the Overall Energy Efficiency of Wood Buildings—An Overview and Future Possibilities

Cabral

Blanchet

2021

Materials

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“…Tian et al (2019) applied the Bayesian network model to identify the most energy-efficient primary cooling systems [ 27 ]. The Bayesian network model was trained using high energy-efficient data in buildings.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting Time‐Series Energy Data in Buildings Using an Additive Artificial Intelligence Model for Improving Energy Efficiency

Truong

Ngo

Pham

2021

Computational Intelligence and Neuroscience

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Building energy efficiency is important because buildings consume a significant energy amount. The study proposed additive artificial neural networks (AANNs) for predicting energy use in residential buildings. A dataset in hourly resolution was used to evaluate the AANNs model, which was collected from a residential building with a solar photovoltaic system. The proposed AANNs model achieved good predictive accuracy with 14.04% in mean absolute percentage error (MAPE) and 111.98 Watt-hour in the mean absolute error (MAE). Compared to the support vector regression (SVR), the AANNs model can significantly improve the accuracy which was 103.75% in MAPE. Compared to the ANNs model, accuracy improvement percentage by the AANNs model was 4.6% in MAPE. The AANNs model was the most effective forecasting model among the investigated models in predicting energy consumption, which provides building managers with a useful tool to improve energy efficiency in buildings.

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“…ŠVOK sistemoms tenka apie 50 %, o apšvietimui -15 % energijos, suvartojamos pastate (Pérez-Lombard et al, 2008). Rinkdamasis, kokią pastato MKS projektuoti pastatui, projektuotojas labai retai atsižvelgia, kad ji vartotų mažai energijos (Tian et al, 2019). Projektuojant ŠVOK sistemas dažniausiai atsižvelgiama į šiuos veiksnius: projektines šildymo (vėsinimo) galias, pagrindines pastate atliekamas veiklas, klimato zoną, be to, vienodai svarbūs reikalavimai mikroklimatui užtikrinti ir pradinės investicijos, mažiausiai svarbios -metinės išlaidos už energiją ir metinis suvartojamos energijos kiekis.…”

Section: Tyrimo Objektasunclassified

Analysis of Equipment Faults in Indoor Climate Systems and Their Detection and Diagnosis Measures

Misevičiūtė

2020

Science - Future of Lithuania

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Indoor climate systems required to provide indoor climate and ensure indoor air quality, failures affect the amount of energy consumed in a building, although insufficient attention is paid to their operation. The energy consumption can be reduced due to ensured proper operation of indoor climate systems avoiding equipment faults. The article reviews scientific articles, those represent typical heating, ventilation and air conditioning (HVAC) systems equipment faults of the most energy intensive office and commercial buildings. Measures of detecting and diagnosis equipment failures as well are identified. A generalized overview of the study area shows the typical faults of the indoor climate system are related to the control of the devices, sensors, deterioration of equipment performance. The most commonly used methods for detecting and diagnosing faults are automated fault detection and diagnosis (AFDD) methods. Possible solutions for troubleshooting HVAC systems are presented.

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An application of Bayesian Network approach for selecting energy efficient HVAC systems

Cited by 36 publications

References 27 publications

A State of the Art of the Overall Energy Efficiency of Wood Buildings—An Overview and Future Possibilities

A State of the Art of the Overall Energy Efficiency of Wood Buildings—An Overview and Future Possibilities

Forecasting Time‐Series Energy Data in Buildings Using an Additive Artificial Intelligence Model for Improving Energy Efficiency

Analysis of Equipment Faults in Indoor Climate Systems and Their Detection and Diagnosis Measures

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