## 7 Conclusions  
Accurate load forecasting ensures the reliable operation of power grids, saves costs, and leads to more sustainable scheduling and marketing plans in electricity market operation. However, integration of nonlinear energy generation from solar and wind energy sources adds to the uncertainty and challenges involved in developing accurate forecasting models. In this paper, we introduce a novel approach by constructing two different scenarios based on the same dataset and adopting sophisticated AI-based models. Preprocessed datasets were fed to the models for several experiments, in which the models were trained with 3 years of half-hourly data and validated with 1 year of half-hourly data to obtained the optimized parameters.  
For _Scenario_1, double hidden layers with 32 nodes and a lookback period of 10 days in 351 epochs resulte in the minimum validation loss. Considering these optimized parameters, day-ahead forecasting was carried out using the test data with one year of half-hourly data. The results demonstrated that the deep networks were highly influenced  
Figure 20: Prediction of demand using FNN, GRU, and LSTM for the whole dataset for 2013: (**a**) first three weeks of January 2013, (**b**) first three weeks of March 2013, (**c**) last week of March 2013, including Songkran festival, (**d**) non-holiday period of June 2013, (**e**) first three weeks of December 2013.  
by the number of hidden layers and number of neurons per layer. In the case of _Scenario1_, the FNN model exhibited superior accuracy to the two RNN-based models. The FNN model achieved the lowest MAPE of 2.47% when predicting only the weekday load, while the RNN-based GRU model exhibited the worst MAPE of 3.37%.  
Similarly, for _Scenario2_, double layers with 64 nodes and a lookback period of 8 days in 99 epochs resulted in the minimum validation loss. The RNN-based GRU model exhibited the best MAPE of 3.44% when testing on the one-year test dataset. A comparative study was conducted to evaluate the forecasting accuracy across different scenarios. When forecasting for non-holiday periods using the dataset from _Scenario1_, the achieved the lowest MAPE of 2.47% is achieved from FNN method. In contrast, when predicting the same test dataset (non-holidays) using the dataset from _Scenario2_, the RNN-GRU model achieved a slightly higher MAPE of 2.71%, approximately 10% higher. As the dataset for _Scenario1_ specifically consisted of non-holiday weekdays, it is evident that _Scenario1_ provides better accuracy compared to the dataset from _Scenario2_.  
Based on these findings, we can implement STLF by categorizing the dataset into two distinct scenarios. _Scenario1_ can be utilized to predict electricity demand for workdays (Monday to Friday), while _Scenario2_ can be employed to forecast demand for weekends (Saturday and Sunday) and holidays with improved accuracy.  
Conceptualization and methodology, S.K. and K.C.; software, S.K.; validation, S.K., P.K. and S.G.; formal analysis, S.K.; investigation, P.K.; writing--original draft preparation, K.C.; writing--review and editing, K.C., S.G. and S.K.; visualization, P.K.; supervision, S.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data presented in this study is available on request from the corresponding author. The data are not publicly available due to the electricity demand dataset for Thailand is not publicly available. Therefore, the author needs to maintain privacy ethics and can not make it public.  
We would like to express our sincere appreciation to [PERSON], and to the EGAT for providing the necessary data used in this research.  
The authors declare no conflict of interest.