## 3 Materials and Methods
### Modeling
#### 3.3.2 Energy Market MCP Estimation
In the study conducted using the short-term data set for the training of the neural network, input data were generated using two-week Load Forecast Plans (LFPs) from TEIAS. The data created for the period between 22 October 2021 and 5 December 2021 were used to train the neural network, as depicted in Figure 10. The neural network trained with the data set created for 6 December 2021 was tested using the data set prepared for that specific date. Figure 11 illustrates the data set used for testing. The Mean Squared Error (MSE) was found to be 0.014, which shows that the approach will provide an accurate forecast of MCP. The estimates of MCP for the period of the test data were made by the ANN before 12:30 of the preceding day, and the hourly rate was formed before 13:50 of the preceding day when the real energy market is announced by EMOC. Estimated and actual MCP are given in Figure 12, which shows that the estimated MCP follows the actual MCP closely for most of the day, with the average MCP being above the minimum energy generation income of renewable power plants. Figure 10: DLFP data used in ANN training. The ANN was found to make an MCP prediction with an average accuracy of 89.88%. The performance of the network during the training and testing phases is shown in Figure 13.  
Figure 11: DLFP data used in ANN test. Figure 12: Comparison of the ANN estimation and the actual MCP in the energy market. Figure 13: Performance of MLP network. The consistency between the MLP model and the output of the training and testing stages can be seen from the regression shown in Figure 14.  
The objective function to be optimized in training the multilayer sensor network is given by Equation (2). Figure 8:0  
In this equation, '\(n\)' represents the number of samples, '\(i\)' represents the index of the data used, '\(t_{i}\)' represents the target output data, and '\(d_{i}\)' represents the output data produced by the network. In each iteration, the network parameters are updated to minimize this objective function. Table 5 shows the comparison between actual hourly MCP and the ANN estimated MCP for the short-term estimation study for a 24-h period. The average hourly accuracy was 89.88%. Although the ANN estimates of hourly MCP are relatively accurate, they could be used in planning daily production. Figure 8:1 Regression between MLP network output and target data. Figure 15 shows the actual values of the market data and the forecast values generated by the model one day in advance. An estimated production target is set by EMOC for renewable energy plants. This is shown as a yellow line in Figure 8:2 If energy is sold when this target is exceeded, the company will earn more revenue than expected. It is, therefore, important to predict one day in advance which hours of the day the market price will be above this target. The market clearing price at the end of the day is shown by the blue line. If the production plan is made for the hours when the red line is above the yellow line, the production plan is made with maximum efficiency. In the box plot, white boxes indicate that the price is better than the forecast, and the graph is upward. Black boxes indicate that the price is lower than the forecast, and the graph is downward. Next-day production and income were estimated more accurately at 99.74%. This would allow a production plan for a HEPP to be created from the estimates of the ANN. In Figure 12, a comparison graph of the actual and ANN-predicted hourly data for 6 December 2021, as provided in Table 6, is presented. The study enabled the management of the hydroelectric power plant (HEPP) to plan based on the 803.2 MWh production capacity available in the dam, considering both maximum and minimum priced hours. With a prediction accuracy of 99.74% for 6 December 2021, the HEPP management achieved 37.21% higher production revenue.