## 2 Related Works
This variation in the MAPE resultsis 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, including our own research team, have produced interesting results published using the same EGAT dataset 2009-2013 recorded for the Bangkok metropolitan region (Table 2). [PERSON] 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. [PERSON] 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, [PERSON] et al. [30] and [PERSON] 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, [PERSON] [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]. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multirow{2}{*}{**MAPE**} & **Prediction** & \multirow{2}{*}{**Data Source**} & **Published Year** & **Reference** \\ \cline{3-5}  & & & **Horizon** & & & \\ \hline \multirow{3}{*}{ANN model} & 2.90\% & 1 h & DSO, Delhi, India & 2018 & [PERSON] et al. [16] \\ \cline{2-5}  & 1.96\% & 1 h & Bandar Abbas, Iran & 2012 & [PERSON] et al. [38] \\ \hline \multirow{3}{*}{CNN-LSTM} & 2.02\% & 1 h & Public dataset, England, USA & 2019 & [PERSON] et al. [39] \\ \cline{2-5}  & 34.84\% & 1 h & UCI ML dataset (households) & 2019 & [PERSON] et al. [40] \\ \cline{1-1} \cline{2-5}  & 1\% & 24 h & Industrial area, China & 2020 & [PERSON] et al. [41] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Variation of prediction accuracy due to the uncertain nature of demand datasets.