AB-Net: A Novel Deep Learning Assisted Framework for Renewable Energy Generation Forecasting

This section thoroughly explains the experiments performed for power generation forecasting using publicly available datasets with the hold-out method to evaluate the performance of the proposed method. We used 70% and 30% of the data for training and testing, respectively, which is a standard data splitting procedure. Next, for classification purposes, model validation was performed via accuracy, recall, and precision. However, time series forecasting is a regression problem; therefore, basic error metrics such as the MSE, RMSE, and MAE were assessed, which are widely used to validate and verify the effectiveness of regression problems.

4.2. Results on Solar Dataset

This section discusses the results obtained over state-of-the-art techniques that include the most popular competitive DL networks such as BiLSTM, CNN-BiLSTM, ED, and AB-Net.

There are several studies that have used different DL approaches for forecasting purposes. The RNN architecture is one of the most employed techniques for forecasting problems, which is capable of remembering the preceding input data to learn the weights of the network. Several variants of the RNN architecture such as LSTM and BiLSTM have been used that have improved the network’s ability to preserve the network states by capturing the long-term sequential dependencies. Initially, LSTM was formed to extend the memory state in RNNs and to enable them to deal with long-term dependencies. Similarly, another form is BiLSTM, where the proceeding input sequences are learned in both the forward and backward directions. In BiLSTM, several layers are stacked to capture the complex features in time series. In the experiments, we firstly analyzed the results obtained over BiLSTM by using its predefined settings. The two layers are stacked together to process the input data, where each layer performs its operations in the reverse direction. The results obtained from BiLSTM are combined in the final layer to produce the final prediction/forecast. BiLSTM was found to be effective in the literature. The MSE value obtained by BiLSTM on the solar dataset was 0.0112. The value is presented in Table 2 , where the RMSE and MAE are also shown. The forecasting graph obtained over BiLSTM is presented in Figure 5 a. Next, the experiments were performed on the hybrid network where CNN and BiLSTM are combined to extract the most important and discriminative features. In this network, the features from multivariate data are extracted through the CNN layers which contain the most important details about the sequential series data. The features obtained through the CNN are forward propagated into BiLSTM to learn them for forecasting purposes. The value obtained for the MSE on the solar dataset was 0.0111, while the other metric values such as the RMSE and MAE are presented in Table 2 . The forecasting graph obtained over CNN-BiLSTM is presented in Figure 5 b.

Next, the ED model was applied, which is also a technique of using BiLSTM for sequence-to-sequence forecasting problems. This technique involves two BiLSTM networks, where one network encodes the sequence, known as an encoder, while the other decodes the input sequence into a target, called a decoder. The encoder takes a single element from the input sequence at every time step by processing it. It collects the information and forward propagates it. The encoder produces an internal state that contains the information about the entire sequence that helps the decoder to carry out accurate forecasting. Finally, the decoder provides the final prediction at each time step. The MSE value obtained with the ED on the solar dataset was 0.0107. The value is presented in Table 2 , where the RMSE and MAE are also presented. The forecasting graph obtained over the ED is presented in Figure 6 a.

The proposed method is a hybrid connection of an AE and BiLSTM, rendering the network more capable of extracting the most important and hierarchical features from the multivariate data. The initial part of the network consists of an AE that takes the input sequence and analyzes it for detailed information collection. After this step, once the information from the AE part is collected, this information is forward propagated into the BiLSTM for final forecasting. In traditional time series data problems, the AE is usually formed by stacking simple LSTM layers that are not effective in encoding long-term dependencies. However, in the proposed method, we create the AE part from the BiLSTM. The output from the AE is forward fed into the BiLSTM to learn the sequence and provide the final prediction/forecast. The first input layer is a BiLSTM that is followed by another BiLSTM layer, which has a small size. The output taken from the encoder part of the AE is fed into the repeat vector, which is a single vector that reshapes it in our BiLSTM network. The value of the MSE obtained on the solar data was 0.0106. The value is presented in Table 2 , where the RMSE and MAE are also presented. The forecasting graph obtained over AB-Net is presented in Figure 6 b.