# A Deep Learning Approach for Short-Term Electricity Demand Forecasting: Analysis of Thailand Data
## 2 Related Works
Hybrid DNN models have been used in electricity demand prediction in addition to LSTM and FNN. To improve the reliability of short-term demand for electricity forecasting, [PERSON] et al. recommended five distinct recurrent neural network architectures. In terms of accuracy, this study revealed that the GRU and bidirectional LSTM model surpassed the conventional FNN and RNN models, indicating the possibility of combining several machine-learning techniques for enhanced forecasting performance [29]. [PERSON] et al. used a hybrid forecasting model for electrical power prediction that incorporates ANN, including BPNN, the generalized regression neural network (GRNN), the Elman neural network, and an optimized model (GA-BP) neural network, in to half-hourly electrical power data of the State of Victoria and New South Wales of Australia [30]. The ensemble approach has lately gained popularity among researchers because of the improved prediction accuracy that has been demonstrated. For the management of the power system, a survey of demand forecasting models was conducted [31]. [PERSON] et al. [32] introduced an adaptive deep-learning load forecasting framework by integrating transformer and domain knowledge. Adaptive-TgDLF effectively combines the transformer model with human insights to enhance short-term electrical load forecasting. It introduces adaptive learning to tackle data scarcity and changing load patterns, marking a significant advancement in this area of research [32]. The researchers explored model predictive control for adjusting power production and distribution strategies in response to predicted demand, thus preventing delays and optimizing resource use. By precisely predicting short-term load demand, services can enhance grid stability, reduce costs, and boost efficiency [33; 34]. The SLTF works for Thailand appear to have only lately surfaced. Hence, numerous studies have been conducted in Thailand to forecast power consumption using various approaches and strategies. Several studies [2; 35; 36; 37; 38; 39] utilizing data sourced from the Bangkok metropolitan region have been published, recorded by the Electricity Generating Authority of Thailand (EGAT) between 2009 and 2013. [PERSON] and [PERSON] implemented calendar elements, including the year, month, day, and hour, as well as seasonal elements like holidays and special days, which were considered to be the factors driving demand in [2], and used them, together with other crucial variables, to construct numerous predictive models. These elements are typically referred to as deterministic variables. When compared to demand on weekends, weekday demand exhibits dramatically distinct patterns. Due to the existence of industrial sector activity on weekdays, weekday demand is specifically much stronger and more consistent than weekend demand. Weekend and weekday holidays both have significantly lower energy demand than weekday demand. In order that the various weekday patterns would be identified, we needed to create dummy variables that represented each day of a week as either weekdays, weekends, or holidays (whether on weekdays or weekends) [40]. Various researchers have generated noteworthy findings based on the identical EGAT dataset spanning from 2009 to 2013. These findings are summarized in Table 1.