Designing an Analytical Tool for Temperature Forecast from the Solar Renewable Energy Computation

Abstract

Being able to accurately predict temperature from the solar power hitting the photovoltaic panels is a key to estimate the various parameters which helps in temperature prediction. The solar power coming to our planet is predictable, but the energy produced fluctuates with varying atmospheric conditions. Usually, numerical weather prediction models are used to make irradiation forecasts. Our study based on back propagation neural network which is trained and tested based on dataset provided. This paper utilizes artificial neural networks for temperature forecasting. Our study based on back propagation neural network which is trained and tested based on dataset provided. In formulating the ANN-based predictive model; three-layer network has been constructed. Suitable air temperature predictions can provide farmers and producers with valuable information when they face decisions regarding the use of mitigating technologies such as orchard heaters or irrigation. Temperature warnings are important forecasts because they are used to protect life and property. Temperature forecasting is the application of science and technology to predict the state of the temperature for a future time and a given location. Temperature forecasts are made by collecting quantitative data about the current state of the atmosphere. In this paper, a neural network-based algorithm for predicting the temperature is presented. The Neural Networks package supports different types of training or learning algorithms. One such algorithm is Back Propagation Neural Network (BPN) technique. The main advantage of the BPN neural network method is that it can fairly approximate a large class of functions. This method is more efficient than numerical differentiation. The simple meaning of this term is that our model has potential to capture the complex relationships between many factors that contribute to certain temperature. The proposed idea is tested using the real time dataset. The results are compared with practical working of meteorological department and these results confirm that our model have the potential for successful application to temperature forecasting. Real time processing of weather data indicate that the BPN based weather forecast have shown improvement not only over guidance forecasts from numerical models, but over official local weather service forecasts as well. Artificial neural networks and the back propagation algorithm used for temperature forecasting in general are explained.