# A Deep Learning Approach for Short-Term Electricity Demand Forecasting: Analysis of Thailand Data
## 2 Related Works  
The nature of the data, the accessibility of previous observations, and the forecasting goal have an impact on the decision to use a time series model. Building efficient time series models requires careful consideration and analysis of trends and seasonality, feature engineering, and model selection [15]. Kernel-based multitask learning has been shown to be effective in addressing electricity demand forecasting difficulties. [PERSON] et al. developed kernels that consider various seasonal patterns to specifically address the complicated task of forecasting individual customer demand [18]. [PERSON] et al. presented the principles of the pattern-similarity-based methods for short-term load forecasting and similar-pattern-based local linear regression models using Polish power system data [19]. The proposed stepwise and lasso regression methods have been suggested to decrease the number of predictors and have shown superior performance when compared to other models such as ARIMA, ES, ANN, and the Nadaraya-Watson estimator. [PERSON] et al. [20] used an MLR model with a day-ahead forecast MAPE of 1.71% to examine the effects of temperature and holiday types. In addition to artificial intelligence, linear regression was contrasted with other conventional and adaptable techniques for selecting weather variables and identical energy prediction, such as seasonal ARIMA (SARIMA) and regression ARIMA (RegARIMA) [20]. [PERSON] et al. evaluated calendar effects and forecast granularity of STLF using regression trees, neural networks, and SVR with root mean square error (RMSE) and normalized RMSE as forecast error metrics [10]. [PERSON] et al. [21] proposed improved adaptive rules for the genetic algorithm, which demonstrated superior performance compared to the traditional genetic algorithm (GA) in optimizing support vector regression (SVR) and enhancingforecasting accuracy for hourly electricity demand. Additionally, it outperformed the extreme learning machine (ELM) model and various artificial neural network models [21]. Certain scholars have utilized machine learning models to improve their performance by enhancing training models, iterations, hyperparameters tuning, and other different techniques. [PERSON] et al. proposed the boosted neural network (BooNN) model, which combines a set of ANNs iteratively, and then compared it with simple ANN and bagged neural networks (BagNN) [22]. Similarly, they proposed the joint bagged-boosted ANNs that train an ensemble of multiple ANN models in parallel, creating individual ANNs and combining the individual ANNs. Then, the generated results of the proposed model were compared with those of single ANN, BoostNN, and BagNN [23]. The attraction of the RNN-based LSTM and GRU networks among researchers is due to their capacity to manage sequential data and dependencies over time and to discover complicated patterns in the data [24; 25]. Recent improvements in computation and algorithms have resulted in the development of DNN as the primary approach for forecasting electricity demand. This is due to their ability to enhance the feature abstraction capability of the model. [PERSON] et al. presented a modified deep residual network with a two-stage ensemble strategy to enhance the generalization capability [26]. When compared to other models, deep learning (DL) experiments possess outstanding abilities in managing complex non-linear relationships, model complexity, and computational efficiency. [PERSON] et al. investigate and evaluate the effectiveness of feed-forward DNN and recurrent deep neural network (R-DNN) models in terms of accuracy and computational efficiency, and the generated results show that the use of transfer function feature analysis with DNN enables higher accuracy [27]. [PERSON] et al. [28] assessed the efficacy of LSTM and FNN models for electricity demand prediction, scrutinizing their preciseness and resilience. Their work underscores LSTM's expertise in capturing complicated long-term patterns in electricity demand data [28].