## 3 Experimental Results and Discussions  
In this paper, we developed two different artificial based load forecasting models. The fuzzy logic model is the main part of the system. The genetic algorithm and ant colony-based techniques were used to increase the performance of the fuzzy forecasting system. Proposed GA-FL and AC-FL load forecasting models were trained with historical electricity load consumption and air temperature data between 2011 and 2014, as explained in Section 2. The training and testing datasets consist of four input variables (L\({}_{\text{dc}}\), L\({}_{\text{wc}}\), L\({}_{\text{cal}}\), and T\({}_{\text{eff}}\)) and one output variable (L\({}_{\text{forecasted}}\)). Membership function type and values were identified by annual changes in the load consumption and weather temperature. The average load consumption values of 2013 are given in Figure 15.  
Figure 14: Choosing new ant route and creation of a new rule base set. Figure 13: Random ant route generation at the first step of the iteration. Load data was fuzzified through using five membership functions that considered the average values of 2013; the fuzzified input and output data (L\({}_{\text{dc}}\), L\({}_{\text{wcr}}\), L\({}_{\text{cal}}\), and L\({}_{\text{forecasted}}\)) are seen in Figure 16.  
In Figure 16, VL denotes the lowest load value, which means very low consumption, L denotes a lower consumption than the normal level, N denotes normal consumption, H denotes a higher consumption than the normal level, and VH denotes the highest load consumption value. Temperature data was also fuzzified using five membership functions. Membership function values were determined considering the thermal comfort values of TS EN ISO 7730, and the fuzzified temperature data is seen in Figure 17[40]. Figure 16: Fuzzified load input and output data. Figure 17: Fuzzified temperature input data. Figure 15: Day-ahead average load consumption values of 2013. In Figure 17, VL denotes the lowest temperature value, which means a very low temperature, L denotes a lower temperature than normal, N denotes a normal temperature, H denotes a higher temperature than normal, and VH denotes the highest temperature value. Temperature data was provided from Turkish State Meteorological Service's database. In this research, we observed a large scaled area in Turkey. Temperature data was collected from six different locations in Turkey. We tried to choose the best locations directly representing Turkey's electricity consumption character. The hourly-based weather temperature data of the selected locations is given in Table 3.  
The average temperature value was calculated with hourly-based temperature data collected from six different locations. The constants (% values in Table 3) that were used in the equation to calculate the average value were defined according to the level of economic development of the cities mentioned above, which was pointed out in the annually published report by the Ministry of Science, Industry, and Technology [41]. After averaging the calculations, the temperature values for the last seven days were used to calculate the temperature parameters as an input variable, while also using the trend analysis methods mentioned above in Section 2.2.1. Both forecasting models were trained and tested with a day-ahead 24 h dataset. The input dataset consisted of the last day of consumption, the last week of consumption, and the consumption and weather temperature trends of the last seven days, which were calculated using the least square method mentioned in Section 2. As a sample, the input dataset used for the training of both proposed models is given in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Time** & **Samsun** & **Ankara** & **Kirkkale** & **Izmir** & **Adana** & **Istanbul** & **Weighted** \\
**(Hour)** & **2\%** & **15\%** & **2\%** & **10\%** & **4\%** & **67\%** & **Avg.