SOLAR PHOTOVOLTAIC OUTPUT POWER FORECASTING USING BACK PROPAGATION NEURAL NETWORK

Solar Energy is an important renewable and unlimited source of energy. Solar photovoltaic power forecasting, is an estimation of the expected power production, that help the grid operators to better manage the electric balance between power demand and supply. Neural network is a computational model that can predict new outcomes from past trends. The artificial neural network is used for photovoltaic plant energy forecasting. The output power for solar photovoltaic cell is predicted on hourly basis. In historical dataset collection process, two dataset was collected and used for analysis. The dataset was provided with three independent attributes and one dependent attributes. The implementation of Artificial Neural Network structure is done by Multilayer Perceptron (MLP) and training procedure for neural network is done by error Back Propagation (BP). In order to train and test the neural network, the datasets are divided in the ratio 70:30. The accuracy of prediction can be done by using various error measurement criteria and the performance of neural network is to be noted

Information Technology and Computer Science

Abstract-Software Effo rt estimat ion involves the estimation of effort required to develop software. Cost overrun, schedule overrun occur in the software development due to the wrong estimate made during the initial stage of software development. Proper estimation is very essential for successful comp letion of software development. Lot of estimation techniques available to estimate the effort in which neural network based estimation technique play a pro minent role. Back propagation Network is the most widely used architecture. ELMAN neural network a recurrent type network can be used on par with Back propagation Network. For a good predictor system the difference between estimated effo rt and actual effort should be as low as possible. Data fro m historic pro ject of NASA is used for training and testing. The experimental Results confirm that Back propagation algorith m is efficient than Elman neural network. Index Terms-Back Propagation Network (BPN), ELMAN Net work, Mean Magnitude of Relative Error (MMRE