FORECASTING OF SOLAR IRRADIATION USING DEEP LEARNING ALGORITHMS

Abstract

This dissertation presents one of many application of Machine Learning (ML) and Deep Learning in the field of forecasting. ML algorithms used Multivariate Linear Regression(MLR), Support Vector Regression (SVR), Feed Forward Neural Network(FFNN) and Layered Recurrent Neural Network(RNN) to make solar irradiation forecasting. The forecasting has been done for the period of ten months in 2021 based on the historical data available for the year 2019 and 2020.MATLAB has been used to develop the ML model. The model developed using the above mentioned algorithms have been compared on the basis of key performance indicators(KPI). The indicators used are mean square error(MSE), Root Mean Square Error(RMSE), Mean Absolute Error(MAE), Mean Absolute Percentage Error(MAPE) and R Square Value (coefficient of determination). This dissertation proposes forecasting of solar irradiation using deep learning algorithm. The algorithm used in this dissertation is sequence to sequence (S2S) algorithm which uses LSTM cell in its encoder and decoder sections. Hence the forecasting has initially been done with LSTM (Long Short Term Memory) in order to make a comparative analysis. Python has been used for developing Deep Learning models. The algorithms are developed on Jupyter notebook using Keras and Tensor flow libraries. The loss function used for optimisation is Mean Square error (MSE) and the key performance Indicator is Root Mean Square Error (RMSE). The results obtained are well within the desirable limits for both ML and Deep Learning.