## 1 Introduction
### Model Approaches
#### 1.4.2 Artificial Intelligence or Data Driven Approaches  
To address the limitations of statistical approaches, data-driven methods have emerged as viable alternatives. Machine Learning (ML) techniques such as support vector regression, decision trees, ensemble learning, and Artificial Neural Networks (ANNs) are commonly used as baseline methods in data-driven approaches. These approaches have the capacity to fully capture complex nonlinear patterns [26]. [PERSON] et al. highlighted the ability of ANNs, fuzzy systems, SVM, and ensemble learning methods to handle nonlinear data. ANNs, particularly Feedforward Neural Networks (FNNs), offer many advantages in comparison to traditional time series models such as ARIMA when handling nonlinear and non-normally distributed data. However, one of the main drawbacks of ANNs is their assumption of independence between inputs and outputs when dealing with sequential data of electric energy consumption [1]. FNNs are one of the most popular structures among ANNs, and are used to model complex input-output relationships through trial-and-error based searching for the best parameter values. Despite their efficacy, FNNs are prone to overfitting, that is, their learning process may not guarantee that the global optimum is reached due to the possibility of the network becoming trapped in a local optimum. Backpropagation learning algorithms are a commonly used method for FNN learning in numerous applications. Deep learning, on the other hand, is a specialized subset of machine learning that uses multiple layers to understand complex pattern data. However, the problem of vanishing or exploding gradients due to long-term dependencies is present when using RNNs. To address this issue, concepts such as gated or Long Short-Term Memory (LSTM) cells and Gated Recurrent Units (GRUs) have been introduced. LSTM cells are well suited for tasks that involve capturing long-term dependencies and sequence-dependent behavior, making them suitable for applications in electricity load demand forecasting [20]. Similarly, GRUs were designed to use a single path, removing the output gating and outputting the cell state directly. The cell state can then be used for both state updates and gating function computations in subsequent steps. This means that the reset gate in GRUs can be considered as a shifted output gate of LSTMs, shifting from the output of the current step (in LSTMs) to the input of the next step (in GRUs) [20]. This paper is an extended version of our previously published research work [22], which focused on the development of a regression model to interpret the impact of temperature variation on electricity demand in Thailand. In the present paper, we use the same dataset while reducing the previous four scenarios into two scenarios, as recommended in [22]. As a continuation of our previous work, [PERSON] et al. [27] tested the same dataset by developing machine learning models through an ensemble learning approach. Therefore, one reason for this extension is to fill the gap with respect to DNN implementation. In this work, we mainly focus on finding the best DNN model on the basis of forecasting accuracy. The major contributions of this paper can be summarized as follows:  
* A comparative study of deep networks for FNN- and RNN-based LSTM and GRU are discussed on the basis of testing and validation accuracy. * Implementation of hyperparameter tuning (number of neurons, layers, dropout, epoch, lookback period, etc.) and a cross-validation strategy to select the best model. * Our results include the finding that increasing the number of hidden layers does not ensure improved forecasting accuracy. * The dataset we used has been tested in various contexts, such as Bayesian [28], regression [22; 28; 29], machine learning [30; 31; 32; 33], and ensemble learning [27]; in this paper, we apply it in the context of deep learning networks.