## 1 Introduction
[9] combined the SOM and the support vector regression (SVR) with adaptive fuzzy rule forecasting and applied it to cases with variable training period lengths. The seasonality of load peaks is addressed through a functional clustering technique. [PERSON] et al. [10] combined the deterministic annealing (DA) clustering for the preprocessing of input data, together with an MLP ANN, to predict day-ahead peak load. [PERSON] et al. [11] classified existing seasonal load data into four patterns using Kohonen NN. Daubechies D2, D4, and D10 WTs were adopted subsequently to predict hourly load. [PERSON] et al. [12] applied clustering techniques to group and label the data set samples. Thus, the prediction of a data point starts with the extraction of the pattern sequence prior to the day to be predicted. Traditionally, most models were based on feed-forward (FF) ANNs trained by modification of the basic back-propagation algorithm. [PERSON] et al. [13] assessed five different learning algorithms for radial basis function (RBF) ANNs to advance their performance in the load forecasting of the ISO-New England market. Typical RBF networks were applied to 24 h electric load forecasting based on SVR, Extreme Learning Machines, and Decay RBF NN. In addition to the shallow and deep, fully connected FF ANNs, which are routinely employed in load forecasting, more complex types, such as the long short-term memory (LSTM), which is a version of recurrent neural networks (RNN), adopt a block structure with a number of gates interacting with the previous and next network state. They are more complex compared to FF ANN. However, they are capable of effectively handling temporal dependencies between variable time series lags. For this reason, they are employed in more complex forecasting tasks, such as the electricity price forecasting in auctions [14]. To this end, convolutional neural networks (CNNs) using convolution to learn patterns within specific time windows are also employed to learn from the data from different perspectives via data shuffling. A review of more recent developments with the use of deep learning (DL) methods in electric power systems is presented in [15]. [PERSON] et al. [16] analyzed the taxonomy of existing DL algorithms applied to different forecasting problems in the electrical utility industry. [PERSON] et al. explored the theoretical advantages of deep learning in power systems research. Supervised, unsupervised, and semi-supervised applications, as well as reinforcement learning tasks, were covered [17]. [PERSON] et al. [18] combined Bayesian probability theory and deep learning in a framework employing clustering in sub-profiles to forecast aggregated net load from the Ausgrid distribution network. Input data for the numerical experiments were collected from smart meters in load centers in Sydney, New South Wales. Additional input from residential rooftop PV outputs was considered to enhance the performance of aggregated net load forecasting. In spite of the significant research effort allocated to the structure of the ANN applied in the load forecasting problem, the structure of input data employed for the training dataset did not receive the necessary degree of attention. The majority of the research effort uses standardized input data from data repositories and performs benchmark tests to assess possible improvements in error metrics. On the other hand, the nature of the load forecasting problem on a country or regional basis is very complicated and affected by multiple factors discussed in the next section, in modes that are not yet well understood [19]. Moreover, day-ahead load forecasting on a country or regional level was challenged by the advent of COVID-19 and the associated shutdown of economic activity, which complicated the prediction. The period from March 2020 to May 2022 had peculiar characteristics due to the COVID-19 pandemic and the measures taken during large time intervals in order to protect public health from the spread of the virus.