Smart Control of Air Climatization System in Function on the Values of Mean Local Radiant Temperature

Cannistraro, G.; Cannistraro, Mauro; Restivo, Roberta

doi:10.1080/23080477.2015.11665651

Cited by 16 publications

(20 citation statements)

References 14 publications

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“…Because of the limitations of our measuring instruments, only the parameters of temperature and relative humidity could be measured at S1, S2, S3, and S4. The measured data should be integrated with acquired air velocity and diffused/direct solar radiation data, which are very important for the evaluation of the local comfort [21][22][23]. In later analysis, we found that the relative humidity did not fluctuate greatly and remained within a certain range; hence, the relative humidity and temperature were not strongly correlated.…”

Section: Model Input and Outputmentioning

confidence: 83%

Thermal Environment Prediction for Metro Stations Based on an RVFL Neural Network

et al. 2018

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With the improvement of China's metro carrying capacity, people in big cities are inclined to travel by metro. The carrying load of these metros is huge during the morning and evening rush hours. Coupled with the increase in numbers of summer tourists, the thermal environmental quality in early metro stations will decline badly. Therefore, it is necessary to analyze the factors that affect the thermal environment in metro stations and establish a thermal environment change model. This will help to support the prediction and analysis of the thermal environment in such limited underground spaces. In order to achieve relatively accurate and rapid on-line modeling, this paper proposes a thermal environment modeling method based on a Random Vector Functional Link Neural Network (RVFLNN). This modeling method has the advantages of fast modeling speed and relatively accurate prediction results. Once the preprocessed data is input into this RVFLNN for training, the metro station thermal environment model will be quickly established. The study results show that the thermal model based on the RVFLNN method can effectively predict the temperature inside the metro station.

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Section: Model Input and Outputmentioning

confidence: 83%

Thermal Environment Prediction for Metro Stations Based on an RVFL Neural Network

et al. 2018

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“…In the building sciences, including the areas of indoor air quality [13,19,[24][25][26], dampness and mold [27], thermal comfort [5,[28][29][30][31][32], acoustics [6,33], green buildings [4,34,35], lighting [36,37], and indoor environmental quality (IEQ) [9,[38][39][40][41][42][43][44][45][46][47][48], chamber and field studies are the predominant experimental approaches for conducting human subject research relating measures of physical conditions to human outcomes. Typically, chambers are the dimensions of a small to medium-sized office and are equipped with building systems (e.g., ventilation system, lighting, sound masking) capable of maintaining stable experimental conditions.…”

Section: Experimental Design In Building Sciencementioning

confidence: 99%

The Spatial and Temporal Variability of the Indoor Environmental Quality during Three Simulated Office Studies at a Living Lab

et al. 2019

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The living lab approach to building science research provides the ability to accurately monitor occupants and their environment and use the resulting data to evaluate the impact that various components of the built environment have on human comfort, health, and well-being. A hypothesized benefit of the living lab approach is the ability to simulate the real indoor environment in an experimentally controlled setting over relatively long periods of time, overcoming a significant hurdle encountered in many chamber-type experimental designs that rarely reflect typical indoor environments. Here, we present indoor environmental quality measurements from a network of sensors as well as building system design and operational data demonstrating the ability of a living lab to realistically simulate a wide range of environmental conditions in an office setting by varying air temperature, lighting, façade control, and sound masking in a series of three human subject experiments. The temporal variability of thermal and lighting conditions was assessed on an hourly basis and demonstrated the significant impact of façade design and control on desk-level measurements of both factors. Additional factors, such as desk layout and building system design (e.g., luminaires, speaker system), also contributed significantly to spatial variability in air temperature, lighting, and sound masking exposures, and this variability was reduced in latter experiments by optimizing desk layout and building system design. While ecologically valid experimental conditions are possible with a living lab, a compromise between realism and consistency in participant experience must often be found by, for example, using an atypical desk layout to reduce spatial variability in natural light exposure. Based on the experiences from these three studies, experimental design and environmental monitoring considerations for future office-based living lab experiments are explored.

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“…The innovative combination of these renewable sources and the use of local air conditioning systems [49,50] will create an energy system that can meet the energy needs of an agricultural greenhouse, thus achieving an optimal climate for plant growing [51].…”

Section: Electrical and Thermal Energy Storage Systemmentioning

confidence: 99%

Design and Concept of an Energy System Based on Renewable Sources for Greenhouse Sustainable Agriculture

et al. 2018

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Bio-organic greenhouses that are based on alternative resources for producing heat and electricity stand out as an efficient option for the sustainable development of agriculture, thus ensuring good growth and development of plants in all seasons, especially during the cold season. Greenhouses can be used with maximum efficiency in various agricultural lands, providing ideal conditions of temperature and humidity for short-term plant growing, thereby increasing the local production of fruit and vegetables. This paper presents the development of a durable greenhouse concept that is based on complex energy system integrating fuel cells and solar panels. Approaching this innovative concept encountered a major problem in terms of local implementation of this type of greenhouses because of the difficulty in providing electrical and thermal energy from conventional sources to ensure an optimal climate for plant growing. The project result consists in the design and implementation of a sustainable greenhouse energy system that is based on fuel cells and solar panels.

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Smart Control of Air Climatization System in Function on the Values of Mean Local Radiant Temperature

Cited by 16 publications

References 14 publications

Thermal Environment Prediction for Metro Stations Based on an RVFL Neural Network

Thermal Environment Prediction for Metro Stations Based on an RVFL Neural Network

The Spatial and Temporal Variability of the Indoor Environmental Quality during Three Simulated Office Studies at a Living Lab

Design and Concept of an Energy System Based on Renewable Sources for Greenhouse Sustainable Agriculture

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