## 3 Proposed RL-HCPV
PV generation and SB datasets [64]: A daily solar resource report for King Abdullah City on the atomic and renewable energy dataset, as part of the renewable resource monitoring and mapping (RRMM) program, was imported. Several features are included in the dataset, such as site, latitude, longitude, date, air temperature, irradiance, and SPV;
3. An EV dataset [65]: Data from the \"Smart-Grid Smart-City Electric Vehicle Trial Data\" were used to generate the EV dataset. The data describes the EV trips during the trial, including odometer readings, speed, distance, and the battery SOC at the beginning and end of each trip;
4. Pricing dataset [66]: Data from SEC were used for RTP and ToU tariffs. The dataset contains the hourly prices (peak, shoulder peak, and off-peak) for each month during the whole year. Preprocessing:

Preprocessing is essential for transforming the imported data into a format that will be more useful for loading into our model. The environment-gathered raw data were not appropriate for further processing due to the possibility that they could contain data that are unnecessary, duplicated, or missing. Thus, it is preprocessed in order to build a clear dataset, which includes label encoding, null value elimination of the situations by deleting duplicate data, and removing special characters, such as ($). Table 3 demonstrates the preprocessing for each dataset. In this study's proposed model, to deal with the uncertainties of PV generations, appliance consumption, EV SOC, availability, and electricity prices, several DL algorithms were applied to predict the future trends of these uncertainty parameters. Several DL algorithms (ANN, LSTM, SVR, multi-target regression, DT) have been employed to decide the most accurate algorithm for each uncertainty parameter. Therefore, the appropriate \begin{table}
\begin{tabular}{c c l} \hline \hline
**Dataset** & & **Preprocessing** \\ \hline \multirow{4}{*}{Appliance consumption dataset} & • & Change the date column to (object) type to extract the day, month, and year; \\  & • & Resample the dataset to an hourly basis; \\  & • & Checking the missing values indicates no null cells; \\  & • & Creating a (total consumption) column. \\ \hline \multirow{3}{*}{PV generation and SB datasets} & • & Change the date column to (object) type to extract the day, month, and year; \\  & • & Extract day, month, year, and time in separate columns; \\  & • & Set the (SPV-updated hourly) as a target column. \\ \hline \multirow{3}{*}{EV dataset} & • & Update the location to ’1’ for home and ’0’ for other; \\  & • & Change the location from ’object’ to ’int’ data type. \\ \hline \multirow{3}{*}{Pricing dataset} & • & Change the date column to (object) type to extract the day, month, and year; \\  & • & Extract day, month, year, and time as separate columns. \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Preprocessing for each datasetDL algorithm is set for each uncertainty parameter (see Table 4). The input for each DL algorithm is shown in Figure 4.

Figure 4: Input experimental profiles: (**a**,**b**) total appliance consumption and PV generation; (**c**) appliance consumption; (**d**) stationary battery SOC; (**e**,**f**) EV availability and SOC; (**g**) ToU; and (**h**) RTP. In this model, each uncertainty parameter was manipulated separately to ensure the most accurate output predictions. The DL models were implemented for each uncertainty parameter using Python. First, an ANN model for appliance consumption was applied using a training dataset in the input layer. Three hidden layers were included, with 32 neurons for each layer and one output layer for the predicted power consumption values. The model displayed the accuracy of mean absolute error (MAE) at 0.21 and mean square error (MSE) at 0.22. In addition, LSTM modelling was applied for PV power generation. The PV generation training dataset was used in the input layer. Three hidden layers were included, with 96 neurons for each layer and one output layer for the predicted PV solar generation values.