Solar Photovoltaic Output Energy Forecasting using Linear Regression and Artificial Neural Network

Sunlight based vitality is a perfect and sustainable power source asset that has progressively turned out to be imperative due to the diminishing utilization of fossil fuel resources over the world. Forecasting output power in a Solar Photovoltaic panel is one of the major challenges under outdoor conditions. In this work, the problem is considered and two methodologies for the estimation of solar photovoltaic output power is analysed. As it is an outdoor power prediction, a historical dataset is collected by considering various parameters such as wind speed, weather, temperature, wind direction, air pressure, and rainfall. The prediction is timed under 5 minute’s basis. The linear regression model based on the Ordinary Least Square (OLS) method minimizes the sum of the squares error (SSE) and provides better results for forecasting. The error between the independent variables and the dependent variables is reduced by using linear regression model. On the other hand, artificial neural network is the method used for forecasting in the statistical approach. The implementation of ANN display is finished with Multilayer Perceptron (MLP) and the preparation technique for neural system is finished by Back Propagation calculation. After the prediction, the accuracy is measured by various error measurement criteria like Mean Absolute Percentage Error (MAPE), Mean Magnitude of Relative Error (MMRE) and Root Mean Square Error (RMSE) and at last MAPE is considered for analysis. The result shows that artificial neural network model brings out better prediction when compared to linear regression model for maximum power prediction.