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Item DESIGN OF A RENEWABLE ENERGY OUTPUT PREDICTION SYSTEM FOR 1000mW SOLAR-WIND HYBRID POWER PLANT.(INTERNATIONAL JOURNAL OF RESEARCH SCIENCE & MANAGEMENT, 2015) Ogherohwo E.P; Barnabas.B; Alemika.T.EProblems associated with non-renewable energy sources such as fossil fuels make it necessary to move to cleaner renewable energy sources such as wind and solar. But the wind and sun are both intermittent sources of energy therefore accurate forecasts of wind and solar power are necessary to ensure the safety, stability and economy of utilizing these resources in large scale power generation. In this study, five meteorological parameters namely Temperature, Rainfall, Dew Point, Relative Humidity and Cloud Cover were collected for the year 2012 and used to predict wind and solar power output in Jos, Nigeria. The study used prediction algorithms such as Regression techniques and Artificial Neural Networks to predict the output of a 1000mW Solar-Wind Hybrid Power Plant over a period of one year. Individual prediction techniques were compared and Isotonic Regression was found to have the highest accuracy with errors of 40.5% in predicting solar power generation and 35.4% in predicting wind power generation. The relatively high levels of error are attributed to several limitations of the research work.Item DESIGN OF A RENEWABLE ENERGY OUTPUT PREDICTION SYSTEM FOR 1000mW SOLAR-WIND HYBRID POWER PLANT.(INTERNATIONAL JOURNAL OF RESEARCH SCIENCE & MANAGEMENT, 2015-08) Ogherohwo E.P; Barnabas.B; Alemika.T.EProblems associated with non-renewable energy sources such as fossil fuels make it necessary to move to cleaner renewable energy sources such as wind and solar. But the wind and sun are both intermittent sources of energy therefore accurate forecasts of wind and solar power are necessary to ensure the safety, stability and economy of utilizing these resources in large scale power generation. In this study, five meteorological parameters namely Temperature, Rainfall, Dew Point, Relative Humidity and Cloud Cover were collected for the year 2012 and used to predict wind and solar power output in Jos, Nigeria. The study used prediction algorithms such as Regression techniques and Artificial Neural Networks to predict the output of a 1000mW Solar-Wind Hybrid Power Plant over a period of one year. Individual prediction techniques were compared and Isotonic Regression was found to have the highest accuracy with errors of 40.5% in predicting solar power generation and 35.4% in predicting wind power generation. The relatively high levels of error are attributed to several limitations of the research work.Item Computational Analysis of Cross Polarization on KU-Band Satellite Links over Jos, Nigeria(FUPRE Journal of Scientific and Industrial Research, 2017) Aminu Ibrahim; Durodola O.M; Ogherohwo E.P; Taddy E. N.; Zhimwang Jangfa T.This paper presents the computational analysis of cross polarization on KU-Band satellite links. The depolarization effects on satellite links are described in terms of cross polar discrimination (XDP). The differential phase shifts mainly responsible for causing depolarization at Ku-band due to scattering by spheroidal raindrops wascomputed. Simultaneous analyses of sample data from Kuband, EUTELSALAT (W4/W7) satellite beacon footprint at a frequency of 12.245 GHZ and elevation angle of 036 0 E over Jos (9.8965 0 N, 8.8583 0 E, 1192M) were analyzed. Also the distribution of one minute rain rate obtained from Davis Vantage Vue Integrated Sensors Suites (ISS) weather station was computed. These data were applied to the ITU-R procedure in recommendation 618-12(ITU-R, 2015) to estimate the cross polarization discrimination due to rain on earth satellite path. The results shows that XPD at lowervalue imply very high incidences and cross talks are expected in the region. As such frequency re-use is difficult in Jos, Nigeria.