# Short Term Electric Load Forecasting Based on Data Transformation and Statistical Machine Learning
## 1 Introduction  
Load forecasting has always been a challenging task that triggers the interest of both academia and the industrial sector. Since the 1960s, auto-regressive (AR) models have been widely used for designing and implementing predictive load forecasting models. These were based on the assumption that the system/substation load at any given time can be satisfactorily described as a linear combination of past load values and other values of a set of exogenous variables. Therefore, they were either plain AR models or AR models with exogenous variables (ARX) and/or AR models with moving average components (ARMA and ARMAX). Such models and variants thereof were used extensively for electric load forecasting [1, 2]. The linearity assumption was relaxed with the development of machine learning-based models such as neural networks [3, 4] and kernel-based methods (e.g., support vector machines [5]). Neural networks are nowadays the most widely used machine learning tool for nonlinear regression in electrical load forecasting. At the same time, support vector regression is gaining popularity in this area [6]. In this paper we focus on the state-of-the-art Artificial Neural Network (ANN) deep learning algorithms, namely the Convolution Neural Nets (CNNs) in order to perform loadprediction. Currently the most successful and popular algorithm in literature for addressing this issue is the use of Long Short Term Memory (LSTM) [7] method, which belongs to the Recurrent Neural Network (RNN) architectural type and exploits the short and long term relationships that exist in a data series in order to build a predictive model. LSTM algorithms are considered to be the most suitable choice for electric load forecasting compared to other neural network architectures. CNNs, on the other hand, are considered to be the preferred choice for image processing related tasks, like image recognition, due to their ability to take advantage of the inherent stationarity usually observed in the pixel data, thus resulting in more accurate models. Nevertheless, it will be shown that CNN-based models can also provide efficient solutions for the electric load forecasting problem and under certain conditions, such as applying proper data preprocessing and analysis, they could even outperform the LSTM-based models. Despite the black box nature of ANN solutions, there is considerable freedom for differentiating model selection choices and parameter tuning, such as the number of epochs, batch size or hidden dimensions and filters (i.e., the number of output filters in the convolution) for the CNN-based models. The proposed approach offers a comprehensive methodology for model selection and parameter tuning resulting in significantly lower forecasting errors compared to the LSTM model. Furthermore, the data transformation which was performed after extensive statistical analysis allows optimal input selection for our models. The most common input in such cases is the previous state (t-1) of the forecasted variable. This leads to higher accuracy for uni-step forecasting applications, but at the same time it lowers the performance of the multi-step ones by introducing several defects. In our case, the input value is derived directly from the results of statistical analysis. It is important to mention that this paper does not focus on the CNN algorithm itself, but rather on the proper statistical analysis (pre-processing) which facilitates the data transformation based on data features (i.e., stationarity) and achieve best possible performance of the algorithm. The methodology and the different techniques employed will be thoroughly described in Section 2. According to the time horizon used for prediction, load forecasting can be classified into very short-term load forecasting (VSTLF), short-term load forecasting (STLF), medium-term load forecasting (MTLF), and long-term load forecasting (LTLF).