## 1 Introduction
There are plenty of studies that have shown the ANN models outperform machine learning and statistical models such as ARIMA [29, 30, 31, 32, 33]; therefore, the focus of this study is on deep learning models. ANNs, a central component of machine learning methods known as deep learning, have expanded the capabilities of machine learning models by enabling complex pattern recognition in multivariable input data. ANNs learn functional relationships and patterns between inputs and outputs without requiring the explicit extraction of these relationships. Moreover, they can handle intrinsic challenges associated with electric power loads, such as periodicity, seasonality, and sequential dependence among electricity consumption data sequences. Long Short-Term Memory (LSTM), introduced by [PERSON] and [PERSON] [34], emerged as a solution to address the backpropagation and long-term dependency issues in Recurrent Neural Networks (RNNs). LSTM has since become one of the most widely used RNNs in time series analysis, including load forecasting [35, 36, 32]. Over time, a diverse range of ANN architectures have been developed to tackle various forecasting problems. These architectures that are commonly used in load forecasting include Fully Connected Networks (FCNs) [37, 38, 39], RNNs [29, 30, 40, 41], Gated Recurrent Units (GRU) [42, 43], Convolutional Neural Networks (CNNs) [44, 45, 46], Bidirectional LSTM networks (BiLSTMs) and Transformers, which have been highly successful in Natural Language Processing (NLP). For instance, [PERSON], et al. [47] proposed a transformer-based model for day-ahead electricity load forecasting. Additionally, hybrid approaches combining different ANN architectures, each with its unique characteristics, have also been used in electricity power load forecasting. A hybrid CNN-LSTM-BiLSTM with attention mechanism [31], a combination of CNN and LSTM [48], and a hybrid BiLSTM-Auto Encoder [49], are a combination of evaluated models in the present study. As an example, [PERSON], et al. [50] have developed a hybrid CNN-LSTM model, where CNN layers are commonly used for feature extraction, while LSTM layers are responsible for sequence learning. Despite the wide range of ANN models mentioned here for electric load forecasting, a comprehensive comparison of their performance is lacking. For example, reference [51] is a comparative study of RNN, GRU, BiLSTM, and LSTM models for the short-term charging load forecasting of electric vehicles. The hybrid CNN-LSTM, Convolutional 1D and 2D, FCNs, and Transformer models are also included in the present study to include most of the state-of-the-art ANN models. Moreover, the effect of available data, features, and the training time for each model is also evaluated, which gives a better understanding of the ANN model's performance. Therefore, this research tries to bridge a critical gap in the literature where a comprehensive comparative analysis of cutting-edge ANNs in prediction tasks has not been thoroughly evaluated or explored considering available data, features, and training time. The rest of this paper is structured as follows: Section 2 provides a concise overview of the ANN models used in this study. In Section 3, the comprehensive case study is presented detailing data collection, data preprocessing, building the ANN models, defining the evaluation metrics, training and testing the models, and finally, the result of experiments. Section 4 provides a discussion of the results in the light of similar research. Ultimately, Section 5 offers the conclusion.