# A Deep Learning Approach for Short-Term Electricity Demand Forecasting: Analysis of Thailand Data
## 7 Conclusions  
Ensuring the equilibrium between electricity production and consumption is crucial for efficient power system operation. Precise load forecasting plays a key role in achieving this balance as it enables the proactive anticipation of fluctuations in both supply and demand, thereby ensuring grid stability. This paper presented three types of deep learning models: RNN-LSTM, RNN-GRU, and RNN. They were each constructed to represent three different scenarios using the same seven-year half-hourly electrical energy demand data  
Figure 14: Electricity demand (MW) on the \(y\)-axis and days on the \(x\)-axis using RNN.  
Figure 13: Electricity demand (MW) on the \(y\)-axis and days on the \(x\)-axis using RNN-GRU.  
from EGAT for STLF. All three models, along with three scenarios, were validated using one year of data to optimize hyperparameters. These tuned parameters were then utilized to day-ahead forecast the demand for the year 2018.  
In Scenario 1, the RNN-GRU model exhibited the lowest validation MAE loss. This model utilized a 5-day lookback period with 128 nodes in a single layer. It achieved the lowest test MAE loss of 214.79 MW and MAPE of 2.08%. For Scenario 2, the RNN-GRU model achieved the lowest validation MAE loss using an 8-day lookback period with 64 nodes in a single layer, resulting in a test MAE loss of 181.63 MW and MAPE of 1.89%. Meanwhile, in Scenario 3, the RNN-LSTM model obtained the lowest-validation MAE with an 8-day lookback period with 128 nodes in a single layer. This model yielded a test MAE loss of 226.76 MW and MAPE of 2.13%. Among the obtained results from three different models using three scenarios, Scenario 2 emerges as the most promising, outperforming the other two scenarios. This superiority can be assigned to its exclusive focus on the weekday demand dataset, which exhibits consistent demand patterns. On the other hand, Scenario 1 encompasses the entire dataset, including weekdays, weekends, and holidays, leading to a higher degree of demand pattern non-linearity, which raises prediction errors. Scenario 3, which comprises only weekend and holidays demand data, shows improved results compared to previous research, likely due to the expanded dataset.  
These findings suggest the potential for implementing STLF by categorizing datasets into three distinct scenarios. Scenario 2 can effectively forecast electricity demand for weekdays (Monday to Friday), while Scenario 3 proves valuable for forecasting demand during weekends and holidays (Saturday, Sunday, and public holidays), demonstrating enhanced accuracy. We did not utilize additional variables such as solar insolation, air humidity, cloud cover percentage, and daily temperature amplitude (maximum-minimum difference) in our study. However, we may consider incorporating these variables in future research to assess their impact on prediction accuracy.  
Conceptualization and methodology, R.K.S. and C.C.; software, R.K.S.; validation, R.K.S., K.C. and C.C.; formal analysis, C.C.; investigation, K.C.; writing--original draft preparation, R.K.S. and K.C.; writing--review and editing, C.C., K.C. and R.K.S.; visualization, K.C. and R.K.S.; supervision, C.C. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. Not applicable. Not applicable. Not applicable. Data Availability Statement: The data utilized in this research are accessible upon request from the corresponding author. However, they are not publicly accessible as the dataset for electricity demand in Thailand is not openly available. Consequently, the author must uphold privacy and ethical considerations, preventing their public release. The recipient acknowledges the EFS Scholarship granted by the Sirindhorn International Institute of Technology, Thammasat University. And we extend our heartfelt gratitude to [PERSON] and the EGAT for their invaluable contribution of the essential data utilized in this study. The authors declare no conflict of interest.