Investigation of the Optimal Position of Wind Sensors and Wind Turbines on a Building: A Computational Fluid Dynamics Study

Chan, Leon; Roslan, Mohd Eqwan Mohd; Mohamed, Hassan

doi:10.11591/ijeecs.v8.i3.pp756-761

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…This study embraces the advancement in IoT in achieving the accumulation of data as in many other research areas such as [6]- [8]. Particularly in this study, the accumulation of data from embedded sensor are of the purpose to calculate the PMV value.…”

Section: Methodsmentioning

confidence: 99%

Investigating Thermal Comfort for the Classroom Environment using IoT

Ramli¹,

Rawi²,

Hijazi³

et al. 2018

IJEECS

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<p>In this modern century where fine comfort is a necessity especially in buildings and occupied space, the study to satisfy one aspect of human comfort is a must. This study encompasses of exploring the physiological and environmental factors in achieving thermal comfort which specifically considering the clothing insulation and metabolic rate of students as well as the deployment of dry-bulb temperature, mean radiant temperature, humidity, and air movement in order to obtain the level of comfort students are experiencing in class. The level of comfort are detected by using ASHRAE 55 to determine the average thermal sensation response through the Predicted Mean Vote (PMV) value. An android application were developed to read input of recognizing clothing level (thickness of clothing) and capturing metabolic rate to cater the inputs for physiological factors, while radiant temperature, humidity and air movement are captured through static sensors set up in the classroom space. This paper analyses both the physiological and environmental factors in affecting students in class and further determine their comfort levels that is a major influencing factor of focus in learning. Through cross referencing collected data from IoT enabled nodes, it is found that both physiological and environmental factors, and the combination of them greatly influence in getting the most comfortable state with PMV value of 0.</p>

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Section: Methodsmentioning

confidence: 99%

Investigating Thermal Comfort for the Classroom Environment using IoT

Ramli¹,

Rawi²,

Hijazi³

et al. 2018

IJEECS

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“…One of the main issues contained in this data is the fact that there are data equal to zero. Because the Weibull distribution is only for data that is greater than zero, this caused several problems for our parameter estimation [15,16].…”

Section: Rayleigh and Weibull Distributionmentioning

confidence: 99%

An Experimental Study of Weibull and Rayleigh Distribution Functions of Wind Speeds in Kosovo

2018

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This paper compares two commonly used functions, the Weibull and Rayleigh distribution functions, for fitting a measured wind speed probability distribution at a given location over a certain period. The monthly and annual measured wind speed data at 84 m height for the years have been statistically analyzed for the country with a large capacity -Kitka. The analysis is made in the case of the implementation of all the predicted capacity of wind turbines and by virtue of the probability of power distribution. The Weibull and Rayleigh probability distribution functions have been determined and their parameters have been identified. The average wind speed and the wind power density have been estimated using both distribution functions and compared those estimated from the measured probability distribution function. The Weibull distribution function fits the wind speed variation better than Rayleigh distribution function. The average wind speed was found to be 4.5 m/s and the average wind power density was 114.54 W/m According to results, we can conclude that such a distribution of winds in this region yields an appropriate average value of wind power.

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