Analysis of factors affecting the solar radiation received by any region

Rathod, Arun Pratap Singh; Mittal, Poornima; Kumar, Brijesh

doi:10.1109/etct.2016.7882980

Cited by 15 publications

(8 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Note that two stations (i.e., L1 and L2) did not record SR measurements. Such dissimilarity would probably be attributed to several factors, including altitude, terrain, air quality, cloud cover, and vegetation that affect the amount of SR received at any place [ 52 ].…”

Section: Resultsmentioning

confidence: 99%

“…However, we noticed that PS values were significantly low (i.e., ≤ 45.83%) for all station-pairs, and therefore, all stations (i.e., JP104, JP107, JP201, JP213, JP311, and JP316) were required for the measurements of SR in the network. Such significant dissimilarity in SR was attributed to the factors including varying topography (i.e., high differences in the station-altitudes from 256 m to 626 m msl), and cloud cover that might affected the amount of SR received [ 52 ].…”

Section: Resultsmentioning

confidence: 99%

“…Considering the PS values, we identified that all stations were necessary for the measurements of SR in the network. Such a low similarity would be due to the several factors, including varying topography and cloud cover that could affect receiving amount of SR over a place [ 52 ].…”

Section: Resultsmentioning

confidence: 99%

See 2 more Smart Citations

Quantifying relations and similarities of the meteorological parameters among the weather stations in the Alberta Oil Sands region

et al. 2022

View full text Add to dashboard Cite

Our objective was to quantify the similarity in the meteorological measurements of 17 stations under three weather networks in the Alberta oil sands region. The networks were for climate monitoring under the water quantity program (WQP) and air program, including Meteorological Towers (MT) and Edge Sites (ES). The meteorological parameters were air temperature (AT), relative humidity (RH), solar radiation (SR), barometric pressure (BP), precipitation (PR), and snow depth (SD). Among the various measures implemented for finding correlations in this study, we found that the use of Pearson’s coefficient (r) and absolute average error (AAE) would be sufficient. Also, we applied the percent similarity method upon considering at least 75% of the value in finding the similarity between station pairs. Our results showed that we could optimize the networks by selecting the least number of stations (for each network) to describe the measure-variability in meteorological parameters. We identified that five stations are sufficient for the measurement of AT, one for RH, five for SR, three for BP, seven for PR, and two for SD in the WQP network. For the MT network, six for AT, two for RH, six for SR, and four for PR, and the ES network requires six for AT, three for RH, six for SR, and two for BP. This study could potentially be critical to rationalize/optimize weather networks in the study area.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

Quantifying relations and similarities of the meteorological parameters among the weather stations in the Alberta Oil Sands region

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Indonesia for example, geographically it is located along the equator with the available monthly solar radiation of between 4.6 kWh/m 2 and 7.2 kWh/m 2 giving an average of 5.12 kWh/m 2 throughout the year [24]. The atmospheric features lead to the variation in solar radiation, including pressure, humidity, temperature, dust particles content, clouds, aerosols, and snow covering [23]. Therefore, the prediction of solar radiation conditions is very important to harvest the solar energy as effective as possible.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Solar Radiation Intensity using Extreme Learning Machine

Suyono

Santoso

Hasanah

et al. 2018

IJEECS

View full text Add to dashboard Cite

The generated energy capacity at a solar power plant depends on the availability of solar radiation. In some regions, solar radiation is not always available throughout the day, or even week, depending on the weather and climate in the area. To be able to produce energy optimally throughout the year, the availability of solar radiation needs to be predicted based on the weather and climate behavior data. Many methods have been so far used to predict the availability of solar radiation, either by mathematical approach, statistical probability, or even artificial intelligence-based methods. This paper describes a method of predicting the availability of solar radiation using the Extreme Learning Machine (ELM) method. It is based on the artificial intelligence methods and known to have a good prediction accuracy. To measure the performance of the ELM method, a conventional forecasting method using the Multiple Linear Regression (MLR) method has been used as a comparison. The implementation of both the ELM and MLR methods has been tested using the solar radiation data of the Basel City, Switzerland, which are available to public. Five years of data have been divided into training data and testing data for 6 case-studies considered. Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) have been used as the parameters to measure the prediction results based on the actual data analysis. The results show that the obtained average values of RMSE and MAE by using the ELM method respectively are 122.45 W/m2 and 84.04 W/m2, while using the MLR method they are 141.18 W/m2 and 104.87 W/m2 respectively. It means that the ELM method proved to perform better than the MLR method, giving 15.29% better value of RMSE parameter and 24.79% better value of MAE parameter.

show abstract

“…The solar radiation intensity on the earth’s surface is highly dependent on latitude, weather, season, time of day, and so forth. 10 It was reported that the maximum hourly radiation intensity received by a horizontal surface ranged from about 840 W/m 2 to 1100 W/m 2 at latitudes from 57° to 25°. 11 The solar radiation intensity is much higher than a body’s normal metabolic heat production.…”

mentioning

confidence: 99%

Effects of Outer Shell Fabric Color, Smoke Contamination, and Washing on Heat Loss through Turnout Suit Systems

Gao

Deaton

Fang

et al. 2022

Textile Research Journal

View full text Add to dashboard Cite

Firefighters frequently have to work in direct solar radiant heat. To reduce firefighter heat stress, the influence of turnout garment properties on heat gain from solar radiation must be understood. This research studied the effects of color, texture, washing, and contamination of outer shell fabrics on heat loss through firefighter turnout fabric materials in simulated solar exposures. It showed that solar radiation could be a major factor in heat loss through turnout suits. Solar radiation equivalent to a sunny day completely reversed heat exchange through the turnout fabric systems, converting a heat loss of about 240 W/m2 to a heat gain exceeding 100 W/m2. Solar radiation caused turnout fabric systems to dry out and this decreased the performance of turnout systems that incorporated bi-component moisture barriers. Most significantly, the color of the outer shell had a major influence on lowering turnout heat loss in solar exposures. Composites with a black-dyed outer shell absorbed more solar energy than composites with lighter colored shell materials. Soot and fire-ground contaminants present on turnout outer shell fabrics also reduced heat loss under solar exposure. The findings of this study answered long-standing questions about the importance of turnout fabric color on heat exchange with the environment. The results provide additional motivation for efficient turnout cleaning practices, not only to reduce potentially toxic exposure to smoke contaminants, but to reduce turnout-gear-related heat strain on firefighters.

show abstract

Analysis of factors affecting the solar radiation received by any region

Cited by 15 publications

References 1 publication

Quantifying relations and similarities of the meteorological parameters among the weather stations in the Alberta Oil Sands region

Quantifying relations and similarities of the meteorological parameters among the weather stations in the Alberta Oil Sands region

Prediction of Solar Radiation Intensity using Extreme Learning Machine

Effects of Outer Shell Fabric Color, Smoke Contamination, and Washing on Heat Loss through Turnout Suit Systems

Contact Info

Product

Resources

About