## 1 Introduction  
Increasingly, information communication technologies and artificial intelligence applications are being used in smart grids. Networks are being built healthier, faults are better detected and resolved, needs are better identified, outages are reduced, forecasts are improved, illegal use is being reduced, and quality is being improved. In the infrastructure of forward-looking programs, new inferences and calculations are being made in light of previous information and planning is improving as a result. The inferences, predictions, and calculations made for future situations, which we call predictions, actually form the basis of all the planning we do.  
Forecasting is an indispensable element of planning. Successful and consistent planning depends on the success of forecasts, predictions, and calculations. The accuracy of the prediction is a very important issue and affects the success of the programming. Therefore, it is important for programmers to focus on prediction, to specialize in it, and to generate and develop new methods in this area.  
Studies in the field of forecasting are very diverse. The majority of these studies have focused on stock market forecasting [1; 2; 3; 4]. In addition, [PERSON] et al. worked on a stock-trading system [5], [PERSON] et al. on predicting the intraday movement direction of a selected index [6], and [PERSON] et al. on air temperature forecasting [7]. In addition, studies on the prediction of meteorological conditions such as air temperature, humidity, solar radiation values, air pollution, etc., are also noteworthy [8; 9; 10; 11].  
In the field of electricity, there are studies on remaining useful life (RUL) prediction and degradation process prediction [12; 13; 14; 15]. When we look at the field of smart grids, there are studies such as energy-consumed estimation, energy-to-be-produced estimation, and solar or wind potential estimation [16; 17; 18].  
In electricity planning, the planning of electrical energy for the following hours and days is very important. Incorrect planning, interruptions, bottlenecks, power fluctuations, and similar negativities directly cause great damage to production, the economy, and the country.  
If power plants can estimate the amount of energy they will produce or sell, it will be easier for load dispatch units to make programming for the following days. Thus, the demanded and produced energy estimates will be balanced and will meet each other, and unplanned energy outages, voltage fluctuations, and system crashes will be prevented. In addition, these estimates are important in the operation, maintenance, fuel use, and similar planning of electricity production facilities and in electricity energy pricing. It should not be forgotten that increasing electricity efficiency will have important effects on the development of the country. Therefore, making the plans and estimates with high accuracy and minimum error rates will direct the measures to be taken and the studies to be carried out.  
We can list electricity forecasts under four headings. 1. Very short-term load forecast (VSTLF), 2. Short-term load forecast (STLF), 3. Medium-term load forecast (MTLF), and 4. Long-term load forecast (LTLF). This study is based on short-term electricity consumption forecasts.  
VSTLF requires only historical loads; STLF usually requires historical loads and weather information; MTLF requires weather and economic information; and LTLF needs weather, economic, demographic, and sometimes land-use information [19].