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Comparative Study of Least Square Methods for Tuning Erceg Pathloss Model
Nnadi Nathaniel Chimaobi,
Charles Chukwuemeka Nnadi,
Amaechi Justice Nzegwu
Issue:
Volume 6, Issue 3, June 2017
Pages:
61-66
Received:
8 January 2017
Accepted:
18 January 2017
Published:
12 June 2017
Abstract: In this paper, a study of two least square error approaches for optimizing Erceg pathloss model is presented. The first approach is implemented by the addition of the root mean square error (RMSE) if the sum of prediction errors is positive otherwise, the RMSE is subtracted from the pathloss predicted by the original Erceg model. In the second method, the composition function of the residue is used to generate the model correction factor that is added to the original Erceg model pathloss prediction. The study is based on field measurement carried out in a suburban area for a GSM network in the 800 MHz frequency band. The results show that the untuned Erceg model has RMSE of 59.27384 dB and prediction accuracy of 59.57243%. On the other hand, the pathloss predicted by the RMSE tuned Erceg model has RMSE of 4.495422dB and prediction accuracy of 97.28188% and the pathloss predicted by the composition function tuned Erceg model has RME of 2.177523 dB and prediction accuracy of 98.7253%. In any case, the two methods are effective in minimizing the error to within the acceptable value of less than 7 dB. However, the composition function approach has better pathloss prediction performance with smaller RMSE and higher prediction accuracy than the RMSE-based approach.
Abstract: In this paper, a study of two least square error approaches for optimizing Erceg pathloss model is presented. The first approach is implemented by the addition of the root mean square error (RMSE) if the sum of prediction errors is positive otherwise, the RMSE is subtracted from the pathloss predicted by the original Erceg model. In the second meth...
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Evaluation of Moving Average Model and Autoregressive Moving Average Model (ARMA) for Prediction of Industrial Electricity Consumption in Nigeria
Idorenyin Markson,
Mfonobong Charles Uko,
Aneke Chikezie
Issue:
Volume 6, Issue 3, June 2017
Pages:
67-73
Received:
29 January 2017
Accepted:
30 March 2017
Published:
12 June 2017
Abstract: In this paper, evaluation of moving average model and autoregressive moving average model (ARMA) for prediction of industrial electricity consumption in Nigeria is presented. Industrial electricity consumption data obtained from Central Bank of Nigeria (CBN) Statistical Bulletin for the year 1979-2014 is used to determine the model parameters and prediction performance in terms of Root Mean Square Error (RMSE) and Coefficient of determination r2 values. The results show that the Autoregressive Moving Average (ARMA) model with coefficient of determination value of 66.0% and RMSE value of 68.628 gives better prediction performance than the Moving Average with coefficient of determination value of 42.6% and value of 84.749. However, coefficient of determination value of 66% is not particularly adequate for acceptable prediction accuracy. In that case, for better prediction accuracy for the industrial electricity consumption in Nigeria, other models may need to be examined apart from the two models considered in this paper.
Abstract: In this paper, evaluation of moving average model and autoregressive moving average model (ARMA) for prediction of industrial electricity consumption in Nigeria is presented. Industrial electricity consumption data obtained from Central Bank of Nigeria (CBN) Statistical Bulletin for the year 1979-2014 is used to determine the model parameters and p...
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Comparative Study of Radius of Curvature of Rounded Edge Hill Obstruction Based on Occultation Distance and ITU-R 526-13 Methods
Mfonobong Charles Uko,
Vital Kelechi Onwuzuruike,
Eke Godwin Kelechi
Issue:
Volume 6, Issue 3, June 2017
Pages:
74-79
Received:
29 January 2017
Accepted:
30 March 2017
Published:
12 June 2017
Abstract: In this paper, comparative study of the ITU 526-13 method and the occultation distance-based method for computing the radius of curvature for rounded edged fitted to the vertex of hilltop obstruction is presented. In the study, path profiles of microwave links with isolated single edged hilltop and another path profile with isolated double edged hilltop are used. The frequencies considered are from the 1.5GHz in the L-band to 36GHz in the K-band. The result show that for all the frequencies considered, the occultation distance for the single edged hilltop remained constant at 80.923 m and that for the double edged hilltop remained constant at 532.203 m. Also, while the radius of curvature by the ITU 526-13 method varies with frequency in the two path profiles considered, the radius of curvature by the occultation distance method remained constant for all the frequencies considered in each of the two path profiles considered. Also, for the double edged hilltop, the radius of curvature from ITU 526-13 method greatly exceeded the radius of curvature by the occultation distance method for all the frequencies considered. The least difference in about 58% at frequency of 1.5GHz and the difference increased to about 115% at 36GHz. However, for the single edged hilltop, the radius of curvature for the two methods are relatively equal for frequencies above 6GHz. Essentially, ITU 526-13 method works well like the occultation distance-based method for the single edged hilltop. Further studies are therefore required to determine the situations under which the ITU 526-13 method can be applied in computing the radius of curvature for rounded edge approximation used in diffraction loss computation.
Abstract: In this paper, comparative study of the ITU 526-13 method and the occultation distance-based method for computing the radius of curvature for rounded edged fitted to the vertex of hilltop obstruction is presented. In the study, path profiles of microwave links with isolated single edged hilltop and another path profile with isolated double edged hi...
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Determination of Yearly Fixed Optimal Tilt Angle for Flat-Plate Photovoltaic Modules Based on Perez Transposition Model
Okon Dominic Ekanem,
James O. Onojo
Issue:
Volume 6, Issue 3, June 2017
Pages:
80-84
Received:
3 January 2017
Accepted:
10 January 2017
Published:
12 June 2017
Abstract: In this paper, a method for the determination of the optimal tilt angle for yearly fixed flat-plate photovoltaic (PV) module at any given location is presented. The method is based on yearly global radiation incident on a horizontal plane as downloaded from NASA website. Futrthermore, PVSyst software that uses transposition model is used to generate the yearly global radiation incident on a tilted plane for various tilt angles, from 0° to 46°. The study is conducted for a health facility in Uyo, Akwa Ibom state, Nigeria with longitude of 7.860761, latitude of 5.011474 and elevation of 67.506 m. The optimal tilt angle is obtained from the quadratic trendline equation fitted to the graph of the transposition factor versus tilt angle. The result is that the optimal tilt angle for the yearly fixed flat-plate PV module at the selected Flocation is 9.71° which gives average yearly transposition factor 1.0105. Essential, the results indicate that about additional 1.05% of solar radiation will be captured per year by tilting the PV module at optimal tilt angle of 9.71°. At any other tilt angle less solar radiation will be captured per year.
Abstract: In this paper, a method for the determination of the optimal tilt angle for yearly fixed flat-plate photovoltaic (PV) module at any given location is presented. The method is based on yearly global radiation incident on a horizontal plane as downloaded from NASA website. Futrthermore, PVSyst software that uses transposition model is used to generat...
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Determination of Optimal Tilt Angle for Biannual Seasonally Adjusted Flat-Plate Photovoltaic Modules Based on Perez Transposition Model
Okon Dominic Ekanem,
James O. Onojo
Issue:
Volume 6, Issue 3, June 2017
Pages:
85-92
Received:
3 January 2017
Accepted:
10 January 2017
Published:
12 June 2017
Abstract: Determination of optimal tilt angle for seasonally adjusted flat-plate photovoltaic (PV) modules based on Perez transposition model is presented. Particularly, two seasons are considered, namely winter and summer. As such, two optimal tilt angles are obtained which requires that the tilt angle the of flat plate PV module will be adjusted twice in a year. The method is based on yearly global radiation incident on a horizontal plane as downloaded from NASA website. Furthermore, PVSyst software that uses transposition model is used to generate the yearly global radiation incident on a tilted plane for various tilt angles, from 0° to 46°. The location used in the study is at longitude of 7.860761, latitude of 5.011474 and elevation of 67.506 m. The results show that the winter season optimal tilt angle is 25.46° the summer season optimal tilt angle is 0° and the yearly fixed optimal tilt angle is 7.16°. The annual transposition factor for the seasonally adjusted tilt angle is 1.05 whereas annual transposition factor for the year fixed tilt angle 1.01. The result amounts to 3.6% improvement is solar radiation capture due to the seasonal adjustment of the tilt angle when compare to the yearly fixed tilt angle.
Abstract: Determination of optimal tilt angle for seasonally adjusted flat-plate photovoltaic (PV) modules based on Perez transposition model is presented. Particularly, two seasons are considered, namely winter and summer. As such, two optimal tilt angles are obtained which requires that the tilt angle the of flat plate PV module will be adjusted twice in a...
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Performance Evaluation of Hata-Davidson Pathloss Model Tuning Approaches for a Suburban Area
Wali Samuel,
Njumoke N. Odu,
Samuel Godwin Ajumo
Issue:
Volume 6, Issue 3, June 2017
Pages:
93-98
Received:
3 January 2017
Accepted:
18 January 2017
Published:
23 June 2017
Abstract: In this paper, comparative study of RMSE-base tuning and multi-parameter-based tuning of Hata-Davidson pathloss model for a suburban area is presented. The study was based on field measurement of received signal strength carried out in a suburban area for a GSM (Global System for Mobile communication) network that operates in the 1800MHz frequency band. The results show that multi-parameter-tuned Hata-Davidson model has better prediction accuracy of 98.70720432% and RMSE of 2.177522885 dB as against the RMSE-tuned Hata-Davidson model with prediction accuracy of 97.42722692% and RMSE of 4.256897001dB. However, the RMSE is quite simple and easier to implement even in embedded systems and systems with limited resource.
Abstract: In this paper, comparative study of RMSE-base tuning and multi-parameter-based tuning of Hata-Davidson pathloss model for a suburban area is presented. The study was based on field measurement of received signal strength carried out in a suburban area for a GSM (Global System for Mobile communication) network that operates in the 1800MHz frequency ...
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Modelling and Forecasting of Residential Electricity Consumption in Nigeria Using Multiple and Quadratic Regression Models
Isaac Amazuilo Ezenugu,
Swinton Chisom Nwokonko,
Idorenyin Markson
Issue:
Volume 6, Issue 3, June 2017
Pages:
99-104
Received:
30 January 2017
Accepted:
30 March 2017
Published:
23 June 2017
Abstract: In this paper statistical analysis of the residential electricity demand in Nigeria is presented Particularly, multiple regression model with one period lagged and quadratic regression model without interactions were used to estimate residential electricity consumption and to forecast long- term residential demand for electricity based on annual data over the period 2006–2014. For the regression models’ explanatory variable, population which is a socio economic variable is used along with temperature which is a climatic variable are used. The results showed that the quadratic regression model without interactions was more accurate due to the fact that it has the highest coefficient of determinant of 93.87 and the least value of Root Mean Square Error (RMSE) of 52.77as compared to the multiple regression model with one period lagged of the dependent variable with coefficient of determinant of 93.50 and RMSE of 53.16. The quadratic regression model was then selected and used to forecast the residential electricity demand in Nigeria for the years 2015 to 2029.
Abstract: In this paper statistical analysis of the residential electricity demand in Nigeria is presented Particularly, multiple regression model with one period lagged and quadratic regression model without interactions were used to estimate residential electricity consumption and to forecast long- term residential demand for electricity based on annual da...
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