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Korachagaon I.,Annasaheb Dange College of Engineering and Technology | Bapat V.N.,Ganga Institute of Technology and Management
Renewable Energy | Year: 2012

The data such as global solar radiation, air temperature, relative humidity, wind and moisture, was collected from 875 stations around the globe. Of which data from 210 stations fairly spread on the earth surface was used to develop the formula for estimating the monthly average daily global radiation on a horizontal surface. In this study, using air temperature, relative humidity, wind, moisture and few derived parameters as independent variables, the most accurate equations have been obtained. The results show that the general formula developed could be used for the estimation of solar radiation with the local site parameters. Thus developed models have been validated with remaining 665 data sites. Finally two candidate models have been proposed. These models are capable of covering 50% of the land area on earth surface between latitude ±30°, enabling estimation accuracy to 93% of sites, with an estimation error (RMSE) limiting to 15%. Thus it is envisaged that, the proposed equations (models) can be used to estimate the monthly average daily global solar radiation in area where the radiation data is missing or not available. This helps in assessing the solar energy potential over necessitated area. © 2011 Elsevier Ltd. Source


Korachagaon I.,Annasaheb Dange College of Engineering and Technology | Bapat V.N.,Ganga Institute of Technology and Management
Journal of Renewable and Sustainable Energy | Year: 2012

Solar energy is one of the most promising renewable energy sources. The availability of the solar energy potential data is very scarce and often not readily accessible. The main objective of this study was to estimate the monthly average global solar radiation at various locations for South America, by the generalized Iranna-Bapat's model. Iranna-Bapat's model is developed to estimate the value of global solar radiation at any location on earth surface. This model uses the most commonly measurable meteorological parameters such as ambient temperature, humidity, wind-speed, moisture for a given location. A total of 35 locations spread across the continent are used to validate this model. The computed values from Iranna-Bapat's model are compared with the measured values. Iranna-Bapat's model demonstrated acceptable results, and statistically displayed lower values of RMSEs. Therefore this model could be a good estimator for predicting the global solar radiation at other locations for South America, where such data is not available. © 2012 American Institute of Physics. Source


Korachagaon I.,Annasaheb Dange College of Engineering and Technology | Bapat V.N.,Ganga Institute of Technology and Management
IEEE-International Conference on Advances in Engineering, Science and Management, ICAESM-2012 | Year: 2012

Estimation of solar radiation is considered as the most important parameter for the design and development of various solar energy systems. But, the availability of the required data is very scarce and often not readily accessible. The foremost objective of the present study was to estimate the monthly average global solar radiation (GSR) at various locations for China province, by the generalized Iranna-Bapat's model. Iranna-Bapat's model is developed to estimate the value of global solar radiation at any location on earth surface. This model uses the most commonly measurable meteorological parameters such as ambient temperature, humidity, wind-speed, moisture for a given location. A total of 28 locations spread across China are used to validate this model. The computed values from Iranna-Bapat's model are compared with the measured values. Iranna-Bapat's model demonstrated acceptable results, and statistically displayed lower RMSE (<10%). Therefore this model could be a good estimator for predicting the global solar radiation at other locations for Chinese province, where such data is not available. © 2012 Pillay Engineering College. Source


Naik U.,k-Technology | Bapat V.N.,Ganga Institute of Technology and Management
Wireless Personal Communications | Year: 2013

Location estimation in a wireless local area network (WLAN) using received signal strength indication (RSSI) has gained considerable attention in recent years. In a conventional RSSI based indoor WLAN localization, mobile node position is estimated through access point (AP) placed at ceiling height. Researchers have proposed solutions for location estimation in line of sight (LOS) scenarios, by installing the AP at a fixed position. This paper demonstrates the improved location accuracy in LOS and obstructed line of sight (OLOS) scenarios by placing the AP at lower heights. The RSSI variations caused by shadow fading for changing AP heights are used to estimate the location accuracy. The localization performance is computed in terms of Cramer-Rao lower bound (CRLB) of range estimate under dynamic environments which is relatively less complex computation technique and is calibration free. Simulation results reveal that the proposed method has better performance than the multilateration with linearization for access point localization algorithm. The minimum mean localization errors are obtained by deploying the access point at 2 m height. The results also demonstrate that the indoor localization accuracy improves for higher order path loss exponent. © 2012 Springer Science+Business Media New York. Source


Naik U.,k-Technology | Bapat V.N.,Ganga Institute of Technology and Management
IET Conference Publications | Year: 2013

The location estimation techniques based on 802.11 b/g wireless local area network (WLAN) have received significant attention in recent years. In this paper we report Cramer-Rao Lower bounds (CRLB) for WLAN user location estimation in a computer laboratory. The CRLBs are computed by using real time radio signal strength measurements performed by user's mobile terminal. This distance error lower bound provides the reference for the accuracy of node localization. The numerical results shows that the mean location error is 1.30 meters and is better as compared to the localization technique using linear least square estimate. The experimental results also demonstrate that the location accuracy improves for higher values of path loss exponent. Source

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