A Neural Network Based Approach to Predict Solar Radiation and Analyze the Suitability of Crops Based on Atmospheric Conditions

Solar energy is one of the basic elements for all renewable and fossil fuels. Solar radiation data is always a necessary basis for the design of any solar energy conversion device and for a feasibility study of the possible use of solar energy in variety of applications. In the existing system the prediction of solar radiation is based on feed forward neural networks. The drawback behind that is they are less accurate. Hence the proposed system is used to predict solar radiation based on a non-linear autoregressive exogenous input model and find the suitability of crops for cultivation based on the atmospheric conditions. The daily average solar radiation for Tiruvallur region is predicted based on climatic factors such as minimum air temperature, maximum air temperature, air pressure and humidity that influence the crop yield for Tiruvallur region. A non-linear autoregressive exogenous input (NARX) is built to predict the solar radiation. The performance of the network is then analyzed by calculating the mean squared error and Regression analysis. The performance analysis is done to determine the accuracy between the actual data and predicted data. Based on the predicted solar radiation and along with other climatic parameters such as the amount of rainfall, soil, duration and suitable months the crops that are suitable for cultivation around Tiruvallur region are analyzed, identified and suggestions are provided to farmers. This work results in benefit of the farmer community around Tiruvallur region.