Short term solar irradiation forecasting using Deep neural network with decomposition methods and optimized by grid search algorithm

Abstract

Due to the variable nature of solar energy, it is necessary to manage a bilateral contract negotiation between suppliers and customers. Therefore, to fulfil this condition, this paper proposed an ensemble approach to forecast the solar irradiation. The signal processing techniques Variational Mode Decomposition (VMD) and Discrete Wavelet Transform (DWT) used with deep neural network to forecast the solar irradiation. The hyperparameters of deep learning model are optimized using grid search optimization with in a suitable tolerable search range. The data of three years (2012-14) is used; where data of year 2012-2013 is used to train model and testing is done on data of year 2014 for New Delhi location. Among all developed models, Bi-LSTM-VMD-Grid Search performance is better in terms of RMSE (5.456W/m2), MAPE (0.948%) and R2(0.924%), Because Bi-LSTM process the information twice and faster than other algorithms and VMD refine the quality of input data better as comparison to DWT. The result of proposed model is compared with existing techniques that predicted the solar irradiation and the forecasted results are more efficient and reliable.