Forecasting solar irradiation using convolutional long short-term memory and feature selection of data from neighboring locations

Abstract

The capacity of renewable energy systems, such as photovoltaic farms, has been steadily increasing owing to the continuous depletion of fossil-based energy sources. Information that can be used to optimize the use of renewable energy is required. This work proposes a novel method that uses data from neighboring locations to perform 24 h-ahead solar GHI forecasting. The approach is helpful in supporting the operation of photovoltaic farms, minimizing the need to collect new data on the target location. This work proposes the Convolutional Long Short-term Memory (ConvLSTM) model to maximize the performance using a feature-rich spatiotemporal dataset. The ConvLSTM model uses Hadamard convolutions in its input-to-state transition and state-to-state transition, allowing it to retain better information. The feature selection methods are used to ensure that only the critical subsets of the dataset are used to develop the model. The subsets identified by the feature selection methods are used on Vector Autoregressive, Support Vector Regression, Multilayer Perceptron, Convolutional Neural Network (CNN), CNN-LSTM and ConvLSTM networks. The winning method is compared to related studies using Stacked-LSTM with transfer-learning, attention-based LSTM and Krill-Herd Algorithm – Support vector Regression (KHA-SVR). The resulting coefficients of determination and root-mean-squared errors were statistically evaluated and compared. Ultimately, the proposed ConvLSTM model performed consistently better across all the seasons and feature subsets.