## 3 Materials and Methods
### Modeling
#### 3.3.2 Energy Market MCP Estimation  
The second part of the study estimates the energy production capacity of the HEPP during the hours when the energy need and price are the highest, using a multi-layer perceptron neural network with the Levenberg-Marquardt learning algorithm. An ANN with 20 nodes in the hidden layer structure and output layer gave the best results. The neural network structure is shown in Figure 9.  
Figure 8: Regression between the output of MLP network and target data. In Figure 9, the inputs of the neural networks are as follows:  
* Day codes: Day codes assigned to the days of the week with a value between 1 and 7. * Time codes: Time codes assigned to the hours of the day: 00:00-08:00 is assigned a value of 1 for the time zone. Between 09:00 and 12:00, it is assigned a value of 2. 12:00-14:00 is the period of low energy consumption and is assigned a value of 3. 14:00-24:00 is the period of high energy consumption and is assigned a value of 4. * n-2, n-1, and n: These parameters are time series of the hourly Load Forecast Plan (LFP). * Exchange rate: The exchange rate of the Turkish Lira to the US Dollar from the Central Bank of the Republic of Turkey (CBRT). Output data is the estimate of daily clearing price data to be published by the Energy Markets Operations Corporation (EMOC). EMOC is a Turkish company responsible for operating and managing energy markets, including electricity and natural gas, ensuring transparency, efficiency, and reliability in energy trading. Analyses were conducted in the study, and normalization functions were derived to produce the most accurate results. The normalization functions used aimed to increase selectivity between maximum and minimum values in the LFP data. The maximum value was identified and set as \"1\", while the minimum value was set as \"0\". This function was applied to all LFP values, producing values between \"0\" and \"1\" to create the LFP data set. Due to the energy market's dependence on various dynamic factors, two data sets, representing long-term and short-term predictions, were used for clearing price forecasting, and the results were compared. The analysis was conducted using the MLP Levenberg-Marquardt (LM) algorithm, which serves as the training function for the artificial neural network. In the literature, many studies have been conducted on electricity price forecasting. Generally, electricity price systems have been linearly anticipated, and linear theories have been used for prediction. However, there are studies indicating that the electricity price system is nonlinear. According to the results obtained in this study, it has been determined that electricity price forecasting involves complexity and nonlinearity. Special time series analysis and normalization methods were employed to capture the MCP results in the current electricity market. A two-stage evaluation study was conducted for MCP, consisting of long-term and short-term data sets. Figure 9: Neural network structure. MCP with Long-Term Data Set  
A two-month period was determined for the long-term training of the neural network. The last two months of the Load Forecast Plans (LFPs) from TEIAS were used as input data for training and testing the neural network. Market clearing price (MCP) directly varies depending on factors such as electricity demand, weather conditions, exchange rates, real-time market conditions, and various other influences. Some discrepancies, approximately around 20%, were observed between the MCP predicted by the neural network and the actual MCP on certain days and hours. Overall, the study results indicated that the neural network achieved an average accuracy of 83.63% in MCP when evaluated comprehensively. MCP with Short-Term Data Set  
Many studies on the estimation of electricity prices have been conducted. Most of the studies use linear estimation or linear theory to estimate the electricity price; however, some have used a non-linear system. This study uses a complex and non-linear system to estimate the electricity price.