## 3 Materials and Methods
### Data Collection  
The first part of the study used artificial intelligence (AI) to estimate the volume of the Cambasi Dam reservoir, the amount of water that will accumulate in the dam, and the maximum possible energy production capacity of the HEPP for the next day. The hourly water flow coming into the Cambasi Dam reservoir was collected for 45 months between 1 January 2018 and 30 September 2021. Data sets were created that used 80% of the 45-month water data to train the ANN and the remaining 20% of the data for testing the ANN. The 36-month training data set for the period between 1 January 2018 and 31 December 2020 is shown in Figure 3a. The 9-month test data set for the period between 1 January 2021 and 30 September 2021 is shown in Figure 3b. The daily energy production was collected for the same period. Figure 4a shows the daily electricity generation capacity of the HEPP with the data set used in ANN training, and Figure 4b shows the daily electricity generation data of the HEPP with the data set used in ANN testing. For the training of the ANN, 36 months of energy production data of the Cambasi hydroelectric power plant were used. When Figure 4 is analyzed, it is seen that the energy generation capacity of the hydroelectric power plant naturally increases in the months when the amount of precipitation is high. Likewise, it is seen that the energy generation capacity increases in the years when the rainfall is higher due to the changing amount of precipitation in Figure 4.  
The specific water consumption (the amount of water consumed per 1 kWh of energy production) of hydroelectric power plants depends on the project characteristics (flow rate and head) and remains constant. Therefore, there is a direct and proportional correlation between the incoming water and the production capacity, which is equal to 1. Figure 2: Danca-2 HEPP location. For the training of the artificial neural network, the water entering the dam reservoir was utilized within the timeframe of 1 January 2018 to 31 December 2020. With this data set, the graph of the daily water inflow to the dam reservoir is depicted in Figure 3a, and the graph of the production capacity of the Darca-2 HEPP is illustrated in Figure 4a. For the testing of the artificial neural network, the water inflow to the dam reservoir from 1 January 2021 to 30 September 2021 was utilized. With this data set, the graph of the daily water inflow to the dam reservoir is shown in Figure 3b, and the production capacity graph of the Darca-2 HEPP is presented in Figure 4b. The study for water forecasting was also tested in different AI models, and the best results were obtained in the study using the MLP Levenberg-Marquardt (LM) algorithm, which is the ANN training function. A comparison of different method results is given in Table 2.  
Figure 3: (**a**) Incoming water data set used in ANN training (**b**) Incoming water data set used in the ANN testing. The second part of the study used an artificial neural network (ANN) to determine the hydraulic power potential of the Cambas Dam reservoir at the hours with the highest MCP for the next day. The study for price forecasting was also tested in different AI models, and the best results were obtained in the study using the MLP Levenberg-Marquardt (LM) algorithm, which is the ANN training function. A comparison of different method results is given in Table 3. \begin{table}
\begin{tabular}{l c} \hline \hline
**Model Used** & **Mean Squared Error (MSE)** \\ \hline ANN LM Model & 0.27 \\ Simple Recurrent Neural Networks (RNN) Model & 0.83 \\ Long/Short Term Memory (LSTM) Model & 0.46 \\ Gated Recurrent Units (GRU) Model & 0.44 \\ Radial Basis Function Network (RBFN) & 0.67 \\ Adaptive Neuro Fuzzy Inference System (ANFIS) & 0.35 \\ Generalized Regression Network (GRN) & 5.71 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of different methods for water estimation. Figure 4: **(a)** Daily energy production data set used for training the ANN (**b**) Daily energy production data used for testing the ANN.