Application of Neural Networks in Weather Forecasting (Published)
Weather Forecasting is the task of determining future state of the atmosphere. Accurate weather forecasting is very important because agricultural and industrial sector largely depend on it. Weather forecasting has become an important field of research in the last few decades. In most of the cases the researcher had attempted to establish a linear relationship between the input weather data and the corresponding target data. The Neural Networks package supports different types of training or learning algorithms. In this paper, the application of neural networks to study the design of neural network technique for Kanyakumary District,Tamil Nadu, India. A total of ten years of data collected for training the net work. The network is trained using the Back propagation Algorithm, Radial Basis Function, Regression Neural Network, Optical Neural Network, and Fuzzy ARTMAP Neural Network. The Fuzzy ARTMAP network can give the best overall results in terms of accuracy and training time. It is better correlated compared to the BPN,RBFN,GRNN and ONN networks. The proposed Fuzzy ARTMAP neural network can also overcome several limitations such as a highly non-linear weight update and the slow convergence rate.
Design of a Renewable Energy Output Prediction System for 1000mw Solar-Wind Hybrid Power Plant (Published)
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.
ESTIMATION OF SUITABLE FIELD WORKDAYS OF PLANTING AND HARVESTING OPERATIONS OF MAIZE PRODUCTION IN BAUCHI STATE NIGERIA (Published)
Estimation of suitable field workdays were determined in Bauchi LGA based on soil moisture content and vagaries weather. A ten year metrological weather information were obtained at Federal Ministry of Aviation, Bauchi from 1999-2009. A computer program was written in Visual Basic 2008 to compute daily soil moisture content for the period beginning May to ending October for each of the ten years and apply tractability conditions to the soil moisture balance to estimate good machinery field workdays and days best for tillage. Out of the 184 days working season for each of the years assessed for trafficability conditions, the year (2001) had the lowest suitable workdays (56 days) with 30.43 percent of the total time. The year (2000) had the highest suitable workdays (100 days) with 54.35 percent. The year (2001 & 2008) had the lowest number of best tillage workdays (29days) with 15.76 percent of the total time and the year 1999 had the highest number of tillage workdays (68days) with 36.96 percent