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].

Hippert 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. Harun 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 Tee 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 Raza 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.

Li 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, Selvi 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 Torabi et al. [38] achieved an MAPE of only 1.96% with the same ANN model. This variation in the MAPE results is due to the high dependence on the geographical region from where the demand comes. For example, electricity from industrial regions is much more stable than that from residential, agricultural, or urban areas. In residential areas, demand is highly fluctuating in nature due to the behavior of local residents. Addressing such uncertainty represents a huge challenge for researchers.

In the context of Thailand, several studies have been conducted to predict electricity demand using various methods and techniques. Several authors, have produced interesting results published using the same EGAT dataset 2009–2013 recorded for the Bangkok metropolitan region. Dilhani et al. [33] used an ANN method to forecast electricity demand based on historical electricity demand and temperature data. However, their results were tested only for one month, while the results in this study are tested for one year. Parkpoom et al. [42] conducted a small study on the effect of temperature on electricity demand using a simple regression model, although its prediction accuracy was poor due to its being a traditional model with few variables. A more advanced model with additional variables was developed and tested in [22]. Similarly, Phyo et al. [30] and Su et al. [31] implemented DNN-based methods to forecast electricity demand for the same dataset. To improve the model’s accuracy, they performed data cleaning and grouped the data into similar days. As measured by the Mean Absolute Percentage Error (MAPE), their results were high compared to similar previous studies.

Weather conditions have a significant impact on short-term electricity demand forecasting, and are commonly incorporated into forecasting models [45]. For short lead times (i.e., of up to six hours), univariate methods that do not include weather variables can achieve competitive performance [11]. Due to the difficulties and high costs involved in accessing weather data, univariate models are often used [8,46].

Regions with similar weather conditions to Thailand can provide useful insights into electricity demand forecasting for Thailand. For example, Ismail [19] investigated the impact of weather variables, holidays, and other factors on daily and monthly demand for Malaysia. Their monthly predictions achieved an MAPE of 1.71%. In another study, the effect of air conditioning in the US was investigated, with a 20% increase in cooling degree days found to increase residential electricity consumption by 1% to 9% during the summer season and 5.4% during peak hours [47].