## 5 Conclusions  
In this study, the effectiveness of state-of-the-art Artificial Neural Networks (ANNs) in time series analysis, particularly focusing on electricity consumption prediction--a critical task in power distribution utilities--is explored. Moreover, to better understand the performance of the ANN models regarding the available training data and the effect of features, the dataset is split into three parts. The models are trained with only electricity consumption one feature, called univariable, and multivariable models include six features, including temperature, sine and cosine of days and years to reflect periodicity in days and years. In comparing our research with prior studies, this study contributes insights into a better understanding of the state-of-the-art ANN model's performance in time series analysis, specifically in electricity consumption prediction--a vital task for power distribution utilities. The present study has practical implications for practitioners and researchersin the efficient operation, management, and maintenance of Critical Infrastructures (CIs) through informed decision-making and strategy development, leveraging state-of-the-art ANN models. For example, in a scenario where available data fall between 2000 and 22,000 and there are not enough features available, a GRU model can be selected with high confidence with good accuracy. When the available data fall between 22,000 and 42,000 LSTM, BiLSTM can be selected with a high confidence of acceptable accuracy. Moreover, a CCN-based model such as Conv1D can be selected, where the computational expenses are significant. **The key findings of this study are as follows:**  
* The LSTM model, increasing data and features, outperforms other models, highlighting that when more data are available, feature inclusion enhances LSTM performance in power prediction. * There is an optimal training dataset size for each model regarding their depth (layers), and increasing or decreasing the data beyond that point leads to overfitting. * For the time series prediction task, when the training data are below 2000 in time series prediction, the performance of ANN models decreases dramatically. * When the training data are in the range of 2000-22,000, the GRU is the best choice regarding accuracy. * Where the training data fall in the range of 22,000-42,000, the LSTM and BiLSTM are good choices in terms of accuracy. * Where the training data are between 42,000 and 360,000, LSTM, and ConvLSTM are good choices in terms of accuracy. * Even though Conv1D and Conv2D did not appear in the top two best-performing models (Conv1D univariable third in 25% and entire data), they are good choices in terms of computational efficiency. * When the range of data training is lower than 22,000, the feature inclusion does not improve the performance of any ANN model. * When the number of training data are more than 22,000, the ANN models do not show any consistent pattern or relationship regarding the inclusion of features. But somehow, LSTM, Conv1D, Conv 2D, ConvLSTM, and FCN benefit from including features. Univariable models like BiLSTM, GRU, and Transformer consistently outperform multivariable models, irrespective of training data size. * This study highlights the computational efficiency of convolutional neural networks in processing one-dimensional inputs like time series sequences. The convolutional 1D univariable model emerges as a standout choice for scenarios where training time is critical, sacrificing only 0.000105 in accuracy, and a threefold improvement in training time is gained. * The shallow ANN models in this study exhibit poor performance with 50% of the data but excel with the smallest and largest datasets. This implies that each ANN model may have an ideal training dataset size concerning their layers and depths, and going beyond or below this point could result in overfitting. This underlines the importance of meticulous model selection, considering factors like available data, type of ANN model, depth, and available features.