The laws of quantum physics were employed in their quantum neural network models, and used with the genetic algorithm (GA) to optimize and find the best sub-optimal network structure. The results of the research showed that the HQENN (Hybrid Quantum Elman Neural Network) model has an acceptable high accuracy, and might be used for short-term load forecasting [17]. [PERSON] et al. (2009) proposed a fuzzy logic short-term load forecasting model using historical weather temperature and a load dataset [18]. [PERSON] et al. (1994) used a hybrid neuro-fuzzy model for short-term load forecasting. They used fuzzy logic to obtain input information for the neural network forecasting model. Historical load and weather temperature data were used for training and testing the load forecasting model [19]. In Turkey, there a large number of studies aim to have the closest results in short-term load forecasting. [PERSON] et al. (2017) published an article about forecasting the electricity demand of Turkey. They developed an hourly demand forecasting method on annual, weekly, and daily horizons using a linear model. Their model was based on sinusoidal variations, without using any climatic or econometric information. Their proposed method was applied to the Turkish Power market data for the period 2012-2014, and the daily and weekly electricity demand horizons were predicted [20]. [PERSON] et al. (2015) presented fuzzy logic short-term load forecasting models. They used fuzzy, adaptive neuro-fuzzy, and hybrid models with historical weather temperature data, seasonal changes, and historical load consumption datasets [21]. [PERSON] et al. (2013) proposed an artificial intelligence-based load forecasting method that used signal processing and artificial neural networks. Their dataset was independent of historical weather condition influences and changes [22]. [PERSON] et al. (2006) presented an artificial neural network load forecasting model to predict the hourly load consumption amount of the Middle Anatolian region. They used historical weather temperature and a load consumption dataset, and the obtained results were compared with regression-based statistical models [23]. [PERSON] et al. (2006) proposed [PERSON]'s recurrent neural network model, and compared their results with other recurrent intelligent architecture models [24]. To date, a considerable body of research has been sought to understand load consumption characteristics. Previous research has demonstrated that several types of exogenous factors such as weather conditions (air temperature, humidity, enlightenment time, etc.) or economic changes and fluctuations directly affect hourly-based electricity consumption. This paper contributes to recent literature on:  
* Flexible forecasting environments and conditions with independently chosen training dataset period seasonal changes,
* Intelligent optimal knowledge base methods for modeling load fuzzy forecasting systems. * Hybrid load forecasting approaches that use genetic algorithm and ant colony optimization methods with fuzzy logic techniques. In this study, we present hybrid genetic ant colony-based fuzzy inference load consumption forecasting models, which are composed of fuzzy logic and nature-inspired optimization methods. One of the most important difficulties when developing a fuzzy logic-based forecasting model is the complexity of defining an optimal rule base when the numbers for the input and membership functions are getting larger. Both prediction models presented in the study use natural-inspired methods and provide a more flexible working environment for the researcher while modeling the forecasting system. The researcher can easily increase or decrease the number of the input-output dataset sizes and membership functions without considering the size of the knowledge base.