LAUSR

Solar power plants are growing tremendously to manage the ever-growing demand for power production and supply in a sustainable manner. Solar irradiance forecasting, being a time series problem, can aid in the planning and design of solar power plants. This work have developed a deep learning forecast model for accurately predicting future values of solar irradiance for given location. The locations have been selected with the help of visual information, which indicates the intensity of solar irradiance. This visual information has been provided by NASA’s Prediction of Worldwide Energy Resources (POWER) project. The proposed model utilizes convolutional layers for extraction of internal representations of solar irradiance time-series data along with the attention-based LSTM network for the identification of temporal dependencies. The study is conducted on solar datasets from two locations for a period of 36 years taken from NASA’s Prediction of Worldwide Energy Resource (POWER) project archive of Renewable Energy. Experiments were conducted with comparisons against various deep learning models. From the study conducted, we analyze the effect of different factors, such as model, optimizer, horizon, to decide upon the final forecast result. It has been observed that the proposed model performed with an R2 score of more than 50 percent which indicate excellent forecast performance. Experimental analysis is also conducted for bias and variance as performance evaluators along with other indicators. The bias and variance components also show superior performance of the proposed model. The convolutional and the attention components enhance the performance of the LSTM model with the bias variance mechanism providing improved generalizability.