## 2 Literature Review  
While statistical methods work well under normal conditions, their performance decreases during changing weather conditions, different sociological and economic conditions, and holidays. For this reason, researchers have turned to new approaches, and artificial intelligence methods have gained importance. The main artificial intelligence methods used for load forecasting are artificial neural networks (ANN), fuzzy logic, support vector machines (SVM), support vector regression (SVR), genetic algorithms (GA), particle swarm optimization (PSO), and ant colony optimization (ACO) [20; 21; 22]. [PERSON] et al. and [PERSON] et al. used an SVM modeling approach as an energy consumption predicting algorithm. Here, analyses were made according to weather conditions, calendar, and usage time prices. The analysis shows that estimating residential energy consumption based on weather, calendar, and time-of-use price is feasible and reliable with sufficient accuracy for daily or hourly estimation for some individual residential uses [23; 24]. According to the literature, artificial intelligence methods are more successful than statistical methods in capturing different features that affect the prediction and in making more accurate predictions. In time-series studies, artificial intelligence methods show success despite various limitations such as the poor fit of statistical forecasting models to nonlinear data, the sensitivity of model parameters, and low model generalization ability. However, most methods typically adopt a predefined nonlinear shape and cannot simulate the real nonlinear relationship [25]. Although machine learning methods were known in the field of artificial intelligence, they were not widely used and were not preferred due to a lack of data. However, with the use of smart meters and their widespread use, a lot of data flow has occurred. As a result, machine learning methods began to be used instead of previous methods and they began to yield successful results. In fact, thanks to the recorded data and developing data science, deep learning, a sub-branch of machine learning, has developed rapidly and has surpassed traditional machine learning in terms of prediction accuracy and efficiency in some areas [26]. An extensive literature study has also been conducted on artificial intelligence, machine learning, and deep learning applications [27]. There are different architectures designed to solve different problems in deep learning. The most well-known of these architectures are auto-encoders, convolutional neural networks (CNN), recurrent neural networks (RNN), long short-term memory (LSTM), restricted Boltzmann machines (RBM), deep belief networks (DBN), generative adversarial networks (GAN), transfer learning, and deep reinforcement learning (DRL). In deep learning, RNNs, which process sequences with temporal relationships using self-feedback neurons, are used specifically for time series data [28]. Although the power consumption model is determined by long-term dependence, the predicted value of RNN is only related to the last value. The vanishing of gradients or exploding occurs frequently, which reduces the prediction accuracy. In recent years, gated cyclic unit LSTM, a sub-branch of RNN, has been frequently used to solve this problem [25]. LSTM has established itself in most time series forecasting studies and has outperformed other short-term load forecasting algorithms used in related studies [29; 30; 31]. Some studies have shown that deep learning-based algorithms such as the LSTM outperform traditional-based algorithms such as the autoregressive integrated moving average (ARIMA) model [32; 33]. As seen in the literature, the LSTM model provides suitable solutions for time series. For this reason, LSTM has been mostly used in deep learning studies on time series and electrical energy forecasting studies in recent years.