A Novel Feature Representation for Prediction of Global Horizontal Irradiance Using a Bidirectional Model

India is ranked third after China and the United States of America in terms of solar energy development. Precise prediction of solar energy is very important for several applications, such as electricity grid management, the trading of solar energy, etc. The Global Horizontal Irradiance (GHI) is often taken as a proxy for solar energy generation and used for the prediction task. A considerable amount of uncertainty is present in solar energy due to its strong dependence on atmospheric conditions, which makes the prediction task challenging. Complex weather conditions, particularly clouds, lead to uncertainty in photovoltaic (PV) systems, making solar energy prediction very difficult. Currently, in the renewable energy domain, deep-learning-based sequence models have reported better results compared to state-of-the-art machine-learning models. Bidirectional Gated Recurrent Unit (BGRU) has not been used earlier in the solar energy domain. In this paper, BGRU was used with a new augmented and bidirectional feature representation. The used BGRU network is more generalized as it can handle unequal lengths of forward and backward context. The proposed model produced 59.21%, 37.47%, and 76.80% better prediction accuracy compared to traditional sequence-based, bidirectional models, and some of the established states-of-the-art models. The testbed considered for evaluation of the model is far more comprehensive and reliable considering the variability in the climatic zones and seasons, as compared to some of the recent studies in India. A bidirectional deep-learning model is a combination of two sequential layers: one layer is trained with the preceding values, referred to as the past context to predict the tth term. This is typically called the forward layer. The other layer uses the future context to predict the tth term. This is known as the backward layer. The main contributions of this paper are the application of bidirectional GRU for the first time to solar energy forecasting, which is shown to be better performing than other common sequence models. A new feature representation with a bidirectional nature is proposed, which further augments the performance of BGRU. The model shows improved performance compared to two state-of-the-art models. The performance of the model is validated on real-life data from six solar stations from three climatic zones and in two seasons in India. Recent forecasting models for renewable energy have been outlined, which are broadly classified into machine-learning-based models and deep-learning-based models. Machine-learning-based models, such as Multilayer Perceptron (MLP), Support Vector Regression (SVR), Random Forest (RF), and Multiple Linear Regression (MLR), have been used for solar irradiation forecasting. Deep-learning-based models, such as Long Short-Term Memory (LSTM) and Convolutional neural network (CNN), have also been used for solar irradiation forecasting. The authors proposed a unique re-sampling technique to design a uni-variate solar PV power forecasting model using machine learning algorithms for a forecasting horizon of length of five minutes to three hours.