Design of a Renewable Energy Output Prediction System for 1000mW Solar-Wind Hybrid Power Plant.

Abstract

Problems 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.