## 1 Introduction  
The fundamental objective of electric power industry deregulation is to maximize efficient generation and consumption of electricity and reduce energy prices. To achieve these goals, accurate and efficient electricity load forecasting is becoming more and more important [1]. Distribution and transmission system operators are based on these forecasts in order to deal with the stochastic variations of the distributed renewable power sources connected to the grid [2]. This holds true both for the aggregate system load (i.e., on a country basis) as well as for the load met by micro-grids. Although the main body of the specialized literature addresses the prediction of the total load on a country, region, or county/community level, significant attention is shifted to the bus load of the transmission and distribution systems, which are more affected by the stochastic nature of individual loads [3]. Moreover, forecasting aggregate system load and electricity price has become a major issue in modern power systems, a pre-requisite for price forecasting [4]. Thus, short-term load forecasting (STLF) is an indispensable tool in power systems planning, maintenance, and management and in smart grid applications [5]. It counts decades of research activity and applications, with a multitude of forecasting models, methodologies, and tools for day-ahead and hour-ahead load predictions. Forecasting techniques can be broadly categorized into (i) statistical or time series-based methods, (ii) physical methods, and (iii) hybrid or ensemble methods. However, machine learning (ML) techniques have by far outperformed the other categories. These include a large variety of artificial neural networks (ANN). They start from the well-known multilayer perceptron (MLP), the support vector machine (SVM) [6], the Markov chain methods, etc. ANN-based forecasting is generally considered very effective because of the ANNs' ability to learn complex, nonlinear relationships [2] and their significant advantage of being universal approximators [7]. The specific application requirements should be considered in the selection of the most suitable forecasting methodology. However, every successful forecasting model should be characterized by low computational expenditure and should be able to incorporate empirical knowledge. Further, it should be flexible and straightforward in the interpretation of its results [4]. The models proposed in the literature can be classified into two categories: trend methods and similar-day approaches [5]. Trend methods interpolate the demand curve as a function of time and extrapolate the curve to predict future demand. The similar day approach tracks similarities between current and historical load curves. Machine learning techniques fit this second category. A training set is formed in order to optimally determine or fit the model parameters. After sufficient training, the tuned model is applied to the test dataset. If the modeling error exceeds the set tolerance, the training set is modified, and the model is trained again. Aiming at the reduction of forecasting errors, many researchers proposed hybrid models. These models combine a clustering algorithm and a forecaster. The clustering algorithm captures characteristic attributes of the data. These include outliers, periodic behavior, and other relationships. The training set is divided into a number of relatively homogenous clusters. Each cluster set is employed to train its own forecaster. This process results in improved training of each forecaster. Time series models or ML models can be employed as forecasters in this process. The self-organizing map (SOM) can be profitably combined with SVM for peak load prediction. [PERSON] et al. exploited an SOM for the categorization of their training data [8]. Separate support vector regression models are applied to each cluster to predict the daily peak load. [PERSON] et al.