Determination of Single Knife Edge Equivalent Parameters for Double Knife Edge Diffraction Loss by Deygout Method

In this paper, the computation of dual knife edge diffraction loss by Deygout multiple knife edge diffraction loss method is presented for  a 6 GHz  C-band microwave link. Also,  the computation of equivalent  single knife edge obstruction that will replace the dual obstruction by giving the same diffraction loss as the dual obstructions is presented. The results shows that for the dual obstructions M1 and M2  the total diffraction loss is 54.57746 dB as computed by the Deygout method. The individual diffraction loss from obstructions M1 and M2 are  32.85901 dB and 21.71845 dB respectively. Furthermore, a single knife edge obstruction located at the middle of the link (a distance of 1275m from the transmitter and receiver) and with line of sight clearance height of 483.5089m will be give the same diffraction loss as the dual knife edge obstructions M1 and M2. Essentially, the line of sight clearance height of the equivalent single knife edge obstruction are much more than the sum of the line of sight clearance height of the two initial obstructions

Prediction of Electricity Generation in Nigeria using Exponential Regression and Cobb-Douglas Models

This study presents prediction of electricity generation in Nigeria using two different statistical models, namely; exponential regression and Cobb-Douglas models. Rainfall and temperature were used as the explanatory variables. Data on electricity generation in Nigeria between 2002 and 2014 were obtained from the Central Bank of Nigeria Statistical Bulletin while Data on rainfall and temperature between 2002 and 2014 were extracted from the National Bureau of Statistics (NBS) abstract. Test of model fitness and forecasting accuracy were done using generic statistical approach which include coefficient of determination and root mean square error. The prediction accuracy of the two statistical models was compared and the best model was selected. The best model was then used to forecast electric power generation in Nigeria for the next fifteen years (2015-2029). Among the two statistical models, Cobb-Douglas model was selected as the best model as it gave the highest value of coefficient of determination (r2=99.85%) and the least Root Mean Square Error (48.57%). Furthermore, the Cobb-Douglas model was used to forecast the electric power generation from 2015 to 2029. The forecasted data shows that power generation in Nigeria in 2029 will stand at 3446.85MWh as against the value of 3249MWh in 2014

Prediction of Electricity Generation in Nigeria using Exponential Regression and Cobb-Douglas Models

This study presents prediction of electricity generation in Nigeria using two different statistical models, namely; exponential regression and Cobb-Douglas models. Rainfall and temperature were used as the explanatory variables. Data on electricity generation in Nigeria between 2002 and 2014 were obtained from the Central Bank of Nigeria Statistical Bulletin while Data on rainfall and temperature between 2002 and 2014 were extracted from the National Bureau of Statistics (NBS) abstract. Test of model fitness and forecasting accuracy were done using generic statistical approach which include coefficient of determination and root mean square error. The prediction accuracy of the two statistical models was compared and the best model was selected. The best model was then used to forecast electric power generation in Nigeria for the next fifteen years (2015-2029). Among the two statistical models, Cobb-Douglas model was selected as the best model as it gave the highest value of coefficient of determination (r2=99.85%) and the least Root Mean Square Error (48.57%). Furthermore, the Cobb-Douglas model was used to forecast the electric power generation from 2015 to 2029. The forecasted data shows that power generation in Nigeria in 2029 will stand at 3446.85MWh as against the value of 3249MWh in 2014