## 1 Introduction
[29] employed deep RNN-gated recurrent unit (GRU) models for short- and medium-term prediction, which attained a MAPE of 3.5% in their forecasts. [PERSON] et al. [30] made short-term load predictions at the individual building level. They applied a density-based clustering method to calculate and compare the inconsistency between the combined load and individual loads. Since the consumers' lifestyle significantly changes the energy consumption pattern, the authors proposed an LSTM-RNN-based load forecasting structure for the load demand dataset. The LSTM and BPNN-T in the top tier outperformed all the other benchmarks: MAPE varied from 8.18% to 8.64% in the predictions. [PERSON] et al. [31] proposed a pooling-based deep-RNN for household load forecasting. They attempted to avoid over-fitting caused by increasing data diversity and dimensions. Their STLF model was tested on 920 residential smart meter datasets in Ireland. The RMSE attained outperformed ARIMA by 19.5%, SVR by 13.1%, and classical deep RNN by 6.5% in terms of RMSE. [PERSON] et al. [32] presented a useful comparison of important research works on typical methods used in electricity load forecasting, with indicative values of statistical metrics. They developed and applied a hybrid method--improved backtracking search optimization algorithm (IBSA)-double-reservoir echo state network (DRESN) in STLF. Mutual information is utilized to eliminate low-significance input features and retain key input features. The DRESN structure aims to increase the diversity of the network. Roulette strategy, adaptive mutation operator, and niche operator are introduced to improve the standard BSA algorithm. The IBSA is applied to optimize several critical parameters in the DRESN neural network. The proposed method outperformed eight popular benchmark models, as tested with North America and PJM load datasets. The decades of development of short-term electricity load forecasting techniques have been invested in flexible and easy-to-use computational tools currently employed by the network operators [33]. However, as reported above, the prediction accuracy needs further improvement regarding the design and implementation of the input training datasets, which have specific peculiarities for each country, depending on the size, climate, and economic activities' diversity and other factors. As seen in the above presentation, modeling error results reported from several research works in short-term electrical load forecasting vary over a wide range of MAPE and nRMSE. This can be attributed to the wide variation of training input data structures and the different types of predictions obtained and the different geographical scales met in the different applications. The focus of the present work is on further improvement and standardization of the training dataset of the day-ahead load prediction. This is accomplished by adding the daily heating and cooling degree-days of a representative location, as well as improving the prediction of the peak load, after carefully studying specific time periods in the Greek system where all models systematically fail. In this process, two standard, popular, and cost-effective FF ANN models are employed for the day-ahead system's load forecasting. The comparison is carried out using consistent metrics and the same data from the Greek system [34]. The hourly actual aggregate electricity load, as reported by the Greek Independent Power Transmission Operator (IPTO) [34] during the five-year period 2017-2021, was employed in the training of the models, along with meteorological data. Testing and validation of the models are carried out for various periods of 2022.