## 2 Related Works  
The universal approximation theorem states that an ANN is capable of accurately approximating any nonlinear function. ANN models have been employed for electricity demand forecasting since the 1990s, and have consistently shown promising results. Computational advancements and new state-of-the-art algorithms in recent years have led to the development of DNNs as the leading method of electricity demand forecasting, which is made possible by increasing the feature abstraction capability of the model. The ability of RNN-based LSTM and GRU networks to handle sequential data and long-term dependencies while extracting the complex patterns in the data has led to their widespread popularity among the researchers [20, 21]. [PERSON] et al. [26] mentioned several important criticisms of ANN techniques; despite these limitations, however, ANN models continue to be an important tool in forecasting electricity demand. Deep neural networks possess the capability to acquire nonlinear combinations of features in their deeper layers [34]. These deep learning methodologies, which involve augmenting standard machine learning neural networks with multiple hidden layers, hold great promise as the most effective approach within the field of machine learning. The fundamental structure of FNNs and RNNs remains the same except for feedback between nodes. FNNs are among the most popular models. [PERSON] et al. [35] implemented an FNN in a comparative study between different data preprocessing schemes, obtaining the best result with a 72 h lag load. This study demonstrated the significance of FNNs in electricity demand forecasting and highlights the importance of choosing appropriate inputs and preprocessing techniques to improve model the accuracy. A study by [PERSON] et al. [36] proposed a multilinear FNN model with 51 inputs, including load lag, hours, day type dummy entries, and temperature. Their model achieved a Mean Absolute Percentage Error (MAPE) of 0.439% with a maximum MAPE of 7.986%, which was observed during the month of December. Another study by [PERSON] et al. [2] presented a model utilizing an FNN trained using a gradient descent algorithm. The inputs for their model included variables such as the day of the week, working day indicator, hour of the day, dew point, dry bulb temperature, and loads for the current day, the previous day, and the previous week. The forecasting accuracy reported by the authors ranged from 3.81% in the spring to 4.59% during the summer. [PERSON] et al. [37] evaluated the performance of LSTM and FNN models in electricity demand forecasting by comparing their prediction accuracy and robustness. They found that the LSTM model outperformed the FNN model in terms of both accuracy and robustness, demonstrating the superiority of the LSTM model in capturing complex long-term dependencies in electricity demand data. In order to enhance the performance of their LSTM model, the authors proposed the use of multiple parallel LSTMs; this model was able to capture the multi-scale dependencies in the electricity demand data, resulting in even better prediction accuracy. In addition to LSTM and FNN, hybrid DNN models have been applied in the area of electricity demand forecasting. For example, to enhance the accuracy of short-term electricity demand forecasting, ref. [4] tested five different recurrent neural network architectures (RNNs). Their study showed that the GRU and bidirectional LSTM models outperformed the traditional FNN and RNN models in terms of accuracy, demonstrating the potential of combining multiple machine learning techniques for improved forecasting performance. The same paper suggested the implementation of hyperparameter testing. However, the accuracy depends on the data variation pattern. For example, [PERSON] et al. [16] achieved a 2.90% MAPE value with an ANN model when using the DSO dataset (Delhi, India) for testing with a 1 h prediction horizon, while [PERSON] et al. [38] achieved an MAPE of only 1.96% with the same ANN model (see Table 1).