## 3 Proposed System
### Service Data Preparation Block  
Data Preprocessing Module (DPPM)  
In this data preprocessing module (DPPM), a specific process of preprocessing the energy usage measurement data is described. When a factory, office, or business site consumes energy, such as electricity or gas, energy measurement sensors, including an LPG meter and flow sensor, collect the energy usage at a consistent interval. For example, LPG (liquefied petroleum gas) measurement data can represent a certain amount of usage data from various sensor-installed sites. Sensor-installed sites refer to factories, houses, office buildings, etc. Since the data are collected from diverse places, the characteristics of the measured data are also different. For example, even though the sites use the same LPG energy, their usage varies over time. Therefore, it is necessary to preprocess the dataset to train the service models and extract the meta-features. To preprocess the measurement data, the system queries data from a database for a certain period of energy usage data from various sites. Since the dataset contains different characteristics from site to site, it is necessary to process the data into the same range. For example, some sites are located in cold areas, which require people to use more energy for heating, while other sites in hot areas do not need to use the same amount of energy \begin{table}
\begin{tabular}{l c c c} \hline \hline \multicolumn{1}{c}{**Model Name**} & \multicolumn{1}{c}{**Deep Leaning**} & \multicolumn{1}{c}{**Main Architecture**} & \multicolumn{1}{c}{**Key Features**} \\ \hline TFT & \multirow{2}{*}{\(\bullet\)} & \multirow{2}{*}{Transformer + LSTM} & Strong performance on complex \\ \cline{1-1} (Temporal Fusion Transformer) & & & time series \\ \hline TF (Transformer) & & Self-Attention & Requires positional encoding for \\ \hline LSTM & \(\bullet\) & Recurrent Neural Network & Used for learning long-term \\  & & dependencies \\ \hline DNN & \(\bullet\) & Multilayer Perceptron & Contains multiple hidden layers \\ \hline CNN & \(\bullet\) & Convolutional Neural Network & Effective in local pattern detection \\ \hline GRU & \(\bullet\) & RNN variant & Lighter and faster than LSTM \\ \hline RNN & \(\bullet\) & Recurrent Neural Network & Used for processing sequential data \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The list of trained time-series forecasting models and their characteristics. for heating. The energy consumption difference over a year among three different sites is represented in Figure 2. The figure shows the daily measurements of LPG usage in three distinctive places. In case (a) of Figure 2, the data range is from 0 to higher than 600, which can be seen as an outlier or an anomaly value, while the data range in case (b) is only from 0 to 1, and case (c) contains multiple consecutive zero spots. The peak values of both cases show spiky patterns, high fluctuations, noises, and straight zeros, which could make it difficult for forecasting models to capture the trend and seasonality of the data, generalize to unknown patterns, and increase the model training time. Therefore, it is important to standardize the various data into a common range to ensure the stability and performance of the models. The preprocessing procedure is depicted in Algorithm 1, and the dataset that we used to preprocess is introduced in Section 4. Algorithm 1 is Preprocessing procedure for data cleaning and interpolation.