## 1 Introduction
A study of the effects of shutting on and off several activities, aiming to assess the calibration capabilities of the prediction models, is still underway [20]. [PERSON] et al. [21] investigated the dependence of forecasting accuracy on the selection of an optimal time-lag value. They comparatively tested a statistical approach using auto-correlation, LSTM, and a heuristic optimization algorithm combined with LSTM. In a comprehensive study involving data from load datasets from Australia, Germany, and America, [PERSON] et al. [22] tested a convolution-based DL model with a densely connected network. The model's backbone is the unshared CNN and a densely connected structure to avoid the vanishing of the gradient. [PERSON] et al. tested various models in temporal convolutional (TCN) and RNN/LSTM architectures for predicting the electricity price on the Hungarian market and electricity load in Montenegro TCN and LSTM layers, both in combination with fully connected layers, demonstrated the best performance, but in cases where all models failed with large mistakes, autoregressive LSTM performed even worse [19]. [PERSON] et al. [23] presented the systematic development of a short-term load forecast (STLF) model using 5-year hourly load time series for an electric power utility in Pakistan. Following the investigation of previously developed models, they addressed the challenges of STLF by comparatively applying multiple linear regression, bootstrap aggregated decision trees, and ANNs. [PERSON] et al. [24] employed electricity consumption data from three cities in Jiangsu, China, to train a DL-based framework, random forest, and gradient boosting machine to forecast the total electricity consumption of 3000 users. To address various factors affecting residential electricity consumption, they used feature engineering. The influencing factors were divided into date-related and air-quality-related factors, weather factors, and local economic factors. As long as the forecasting is applied to large regions or a country level, the attainable accuracy is inferior. This is true, especially when the error metrics are applied to the hourly values of power demand. [PERSON] et al. [25] applied a stacked noise suppression auto-encoder (SDA) model and a class of DNN to forecast the hourly electricity price. The datasets were compiled from hubs in five U.S. states. Two types of forecasting, online hourly forecasting and day-ahead hourly forecasting, were examined. MAPE values in the range from 2.51% to 46% were reported, depending on the price fluctuation, which was very high in January and very low in April 2014. [PERSON] et al. [26] employed a DNN for forecasting day-ahead electricity consumption. Ninety days of data from the Iberian utility market were employed for training the multilayer DNN. Various combinations of activation functions were tested, aiming at improved MAPE, taking into account the weekday and weekend variations. The functions tested include Sigmoid, rectifier linear unit (ReLU), and exponential linear unit (ELU). Weekday MAPE ranged between 2.1 and 3.9%. Weekend MAPE ranged between 1.3 and 2.5%. [PERSON] and [PERSON] [27] investigated the feasibility of the application of the feed-forward DNN and recurrent-DNN models utilizing datasets from ISO New England. The proposed models obtained the least daily demand prediction MAPE errors of the order of 1% in the spring season. The highest errors were observed in the summer. They were attributed to the unexpected electricity consumption exceedance caused by high temperatures and social events. [PERSON] et al. [28] combined CNN and K-means algorithms to predict hourly load. They applied the K-means algorithm to a 1.4 million electrical load records dataset, restructuring it into several subsets. Afterward, these subsets were input to CNN for training and testing. The results were promising, attaining 3% MAPE during summer and 7.4% MAPE during winter. [PERSON] et al.