Optimization of Indoor Thermal Comfort Parameters with the Adaptive Network‐Based Fuzzy Inference System and Particle Swarm Optimization Algorithm

Li, Jing; Yin, Shenghua; Shi, Guangsi; Wang, Li

doi:10.1155/2017/3075432

Cited by 11 publications

(7 citation statements)

References 19 publications

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“…There are many works on thermal comfort inside buildings [10,11]. Many authors focus in particular on the influence of the applied ventilation system and air velocity on the comfort of users [12,13].…”

Section: Figurementioning

confidence: 99%

Airflow Fluctuation from Linear Diffusers in an Office Building: The Thermal Comfort Analysis

et al. 2021

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In buildings, the HVAC systems are responsible for a major part of the energy consumption. Incorrect design or selection of the system and improper installation, operation, and maintenance of the systems’ elements may result in increased energy consumption. It is worth remembering that the main aim of the appropriate system is to maintain the high quality of the indoor environment. Appropriate selection of the HVAC solution ensures both thermal and quality parameters of the air, independently of the internal and external heat loads. The microclimate of a room is affected not only by air temperature, humidity, and purity, but also by air velocity in the occupied zone. The proper air velocity distribution prevents discomfort, particularly at workstations. Based on the measurements in the office building, an analysis of velocity profiles of air supplying two different types of linear diffusers was carried out. The analysis was made based on the results of measurements performed with thermoanemometers in the actual facility. During the study, temperature of the supply air was lower that the air in the room. Analysis was focused on the airflow fluctuation and its impact on the users’ comfort. This is an obvious topic but extremely rarely mentioned in publications related to air diffusers. The results show the importance of air fluctuation and its influence on the users’ comfort. During the measurements, the instantaneous air velocity for one of the analyzed types of the diffuser was up to 0.34 m/s, while the average value from the period of 240 s for the same measuring point was relatively low: it was 0.19 m/s. Only including the airflow variability over time allowed for choosing the type of diffuser, which ensures the comfort of users. The measurements carried out for two linear diffusers showed differences in the operation of these diffusers. The velocity in the occupied zone was much higher for one type (0.36 m/s, 3.00 m from diffusers) than for another one (0.22 m/s, 5.00 m from diffusers). The improper selection of the diffuser’s type and its location may increase the risk of the draft in the occupied zone.

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Section: Figurementioning

confidence: 99%

Airflow Fluctuation from Linear Diffusers in an Office Building: The Thermal Comfort Analysis

et al. 2021

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“…erefore, it has been applied in the prediction of the stock price index, the blood glucose concentrations [24], the current blockchain technology [25], the wind generation [26], and so on. e BP neural network is a commonly used timeseries and nonlinear prediction model applied in prediction of short-term wind power, indoor temperature, wind speed [27], and hydraulic press machine.…”

Section: Models and Territoriesmentioning

confidence: 99%

Forecasting Primary Energy Requirements of Territories by Autoregressive Integrated Moving Average and Backpropagation Neural Network Models

Pan

2019

Mathematical Problems in Engineering

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Forecasting energy data, especially the primary energy requirement, is the key part of policy-making. For those territories of different developing types, seeking a knowledge-based and dependable forecasting model is an essential prerequisite for the prosperous development of policy-making. In this paper, both autoregressive integrated moving average and backpropagation neural network models which have been proved to be very efficient in forecasting are applied to the forecasts of the primary energy consumption of three different developing types of territories. It is shown that the average relative errors between the actual data and simulated value are from 4.5% to 5.9% by the autoregressive integrated moving average and from 0.04% to 0.47% by the backpropagation neural network. Specially, this research shows that the backpropagation neural network model presents a better prediction of primary energy requirement when considering gross domestic product, population, and the particular values as predictors. Furthermore, we indicate that the single-input backpropagation neural network model can still work when the particular values have contributed most to the energy consumption.

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“…Due to documents variability in this type of certificate system that portrays buildings' sustainability, this paper aims to analyze them in detail, using the key sustainability criteria obtained from ISO 21929-1 as a basis. Among the methods already executed to assess the effectiveness of sustainability certification systems, stands out the "environmental performance assessment" and "comparative analysis," being the latter considered the most appropriate (Li et al 2017). Furthermore, the main objective of this paper is to perform a qualitative analysis of the content of sustainability certification systems guides using a neutral reference -ISO 21929-1, and not to examine the certification system based on score or classification.…”

Section: Introductionmentioning

confidence: 99%

Qualitative Analysis of Sustainability Certification Systems Based on ISO 21929-1

Polli

Sá

Biju

2022

UPjeng

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Sustainability certification systems are parameters used to measure the extent to which the building meets the necessary sustainable requirements. Currently, there are several types of sustainability certificates, in addition to regulatory standards. Therefore, this work sought to analyze the most influential sustainability certificate systems in the market, using the qualitative analysis of content created by Bardin and based on ISO 21929-1, with fourteen aspects, as a parameter for comparison, the focus of the study was adapted to this method to have a better understanding of the behavior of these systems. Some conditions were imposed to narrow down the number of certification systems, four systems were selected to be analyzed. After the qualitative analysis, it was possible to identify that DGNB (Deutsche Gesellschaft für Nachhaltiges Bauen) is the only one that addressed all the fourteen aspects brought by ISO 21929-1, HQE (Haute Qualité Environnementale) and LEED (Leadership in Energy and Environmental Design) addressed fewer aspects, HQE highly addresses them. However, LEED has a more detailed and clear approach to the aspect related to the Internal conditions and air quality. Lastly, BREEAM addresses thirteen of the fourteen aspects of ISO 21929-1, and most of its content addressed is from the environmental area.

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Optimization of Indoor Thermal Comfort Parameters with the Adaptive Network‐Based Fuzzy Inference System and Particle Swarm Optimization Algorithm

Cited by 11 publications

References 19 publications

Airflow Fluctuation from Linear Diffusers in an Office Building: The Thermal Comfort Analysis

Airflow Fluctuation from Linear Diffusers in an Office Building: The Thermal Comfort Analysis

Forecasting Primary Energy Requirements of Territories by Autoregressive Integrated Moving Average and Backpropagation Neural Network Models

Qualitative Analysis of Sustainability Certification Systems Based on ISO 21929-1

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