# Short Term Electric Load Forecasting Based on Data Transformation and Statistical Machine Learning
## 1 Introduction
The forecasting horizon varies from 5 min, to one day, to two weeks or to three or more years; depending on the planning or operational function it supports [8]. Across literature a wide range of methodologies and models have been proposed to improve the accuracy of load forecasting, yet most of them are based upon aggregated power consumption data at the system (top) level with little to no information regarding power consumption profiles at the customer class level. This approach was acceptable till now, since the operational focus until recently had been on the bulk transmission system and the wholesale energy markets. Since the interest now is shifted towards the distribution system and the efficient integration of Distributed Energy Resources (DERs) connected close to the edge of the grid, this approach is not sufficient anymore. Hence, distribution substation load forecasting becomes a necessity for the Distribution System Operators (DSOs). Forecasting on distribution feeder has not been widely examined across literature. Low Voltage (LV) distribution feeders are more volatile compared to the high voltage (HV) ones, since they consist of low aggregations of consumers [9]. One approach is to adopt the forecasting techniques and models that are currently employed at higher voltage levels. The main forecasting research in these areas has been presented in [10,11], that apply both ARIMAX and ANN methods to a single LV transformer (consisting of 128 customers) for forecasting the total energy and peak demand. In their method they take into account historical weather data and they achieve MAPEs of 6-12%. In [12] a three-stage methodology, which consists of pre-processing, forecasting, and post-processing, was applied to forecast loads of three datasets ranging from distribution level to transmission level. A semi-parametric additive model was proposed in [13] to forecast the load of Australian National Electricity Market. The same technique was also applied to forecast more than 2200 substation loads of the French distribution network in [14]. Another load forecasting study on seven substations from the French network was performed in [15], where a conventional time series forecasting methodology was utilized. In [16], the authors proposed a neural network model to forecast the load of two French distribution substations, which outperformed a time series model. It is focused on developing a methodology for neural network design in order to obtain a model that has the best achievable predictive ability given the available data. Variable selection and model selection was applied to electrical load forecasts to ensure an optimal generalization capacity of the neural network model. Exponential smoothing model and its variations, such as double and triple seasonal exponential smoothing ones, have also showcased good results [17] but are less popular in real-life applications due to their inability to accommodate exogenous variables. Another popular approach, denoted as hybrid, is to combine different models as in [1] where Principal Component Analysis (PCA) and Multiple Linear Regression (MLR) are combined for daily load predictions. For LV networks accurate load forecasting can be employed to support a number of operations, including demand side response [18], storage control [19; 20] and energy management systems [21; 22]. Load forecasting for low scale consumers, such as residential loads, is valuable for home energy management systems (HEMS) [23] while house level load forecasting can play a significant role in the future Local Energy Markets (LEM) which will facilitate the energy transactions among participants [24]. Load forecasting has been implemented for either the HV or system level and typically consists of the aggregated demand of hundreds of thousands or millions of consumers. Such demand is much less volatile than the LV demand, and hence, is easier to predict.