three detailed levels.Figure 7.12 Architecture of ANFIS network.Figure 7.13 Hyperplane SVM architecture.Figure 7.14 Architecture of Kernel-type SVM.Figure 7.15 Flowchart depicting data pre-processing.Figure 7.16 Different network architectures used in deep learning.Figure 7.17 Unsupervised pre-trained network architecture.Figure 7.18 Deep CNN architecture.Figure 7.19 RNN architecture.Figure 7.20 Support vector machine network structure.Figure 7.21 Prediction comparisons between various models.Figure 7.22 Daily variation of averaged wind speed.Figure 7.23 Structure of the proposed ANN model.Figure 7.24 Comparison between the measured and predicted wind speed.8 Chapter 8Figure 8.1 LSTM with four interacting layers.Figure 8.2 No. of epochs vs. loss.Figure 8.3 Actual and predicted test value.Figure 8.4 Visualization over full data.Figure 8.5 Plot over small part of the data.Figure 8.6 Plot over very small part of the data.Figure 8.7 GRU.Figure 8.8 Number of epochs vs. loss.Figure 8.9 Plot of actual and predicted test value.Figure 8.10 Visualization over full data.Figure 8.11 Plot over the small part of the data.Figure 8.12 Plot over very small part of data.Figure 8.13 Bidirectional LSTM.Figure 8.14 No. of epoch vs. loss.Figure 8.15 Actual and predicted test value.Figure 8.16 Visualization over full data.Figure 8.17 Plot over small part of the data.Figure 8.18 Plot over very small part of the data.
9 Chapter 9Figure 9.1 AMI architecture.
10 Chapter 10Figure 10.1 Traditional RNN unrolled over t timesteps [12].Figure 10.2 LSTM cell [16].Figure 10.3 GRU cell [12].Figure 10.4 ConvLSTM cell.Figure 10.5 Bidirectional RNN.Figure 10.6 Demonstration of the sliding window approach.Figure 10.7 Predictions done by models trained with window size = 6.
11 Chapter 11Figure 11.1 Classification of biofuels.Figure 11.2 AI-based biodiesel model.
List of Tables
1 Chapter 1Table 1.1 Eigenvalues of six-phase synchronous generator.Table 1.2 Eigenvalues of three-phase synchronous generator.
2 Chapter 4Table 4.1 Some important applications of ANN technology for the development of b...Table 4.2 Some important applications of combination of artificial neural networ...
3 Chapter 5Table 5.1 Brief summary of current research in battery energy storage systems.Table 5.2 Model parameters for SoC estimation [20].Table 5.3 Performance metrics for regression models.
4 Chapter 6Table 6.1 Models for multi-step wind forecasting.Table 6.2 Ensemble models for WPF.Table 6.3 Miscellaneous DL models for WF.
5 Chapter 8Table 8.1 Comparison of the methodologies with the parameters.
6 Chapter 10Table 10.1 Training and test size for generated dataset from each window size.Table 10.2 Performance of all trained models.Table 10.3 Statistical analysis of the prediction done by LSTM model (widow size...
Guide
1 Cover
5 Preface
7 Index
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