# Short Term Electric Load Forecasting Based on Data Transformation and Statistical Machine Learning
## 1 Introduction
The availability of data from Advanced Metering Infrastructure (AMI) systems enables the use of novel approaches to the way load forecasting is performed, ranging from the distribution level to even the residential scale. In existing literature, researchers have focused on: (1) longitudinal and (2) cross-sectional grouping methods trying to handle efficiently load data. Longitudinal grouping refers to identifying time periods with similar load patterns, which are derived from statistical analysis based on historical data. On the other hand, cross-sectional grouping refers to the aggregation of customers with similar consumption characteristics. In [37] the authors examined six methods which are usually employed in large-scale energy systems to predict a load similar to that of a single transformer. The models that were investigated were ANNs, AR, ARMA, autoregressive integrated moving average, fuzzy logic, and wavelet NNs for day-ahead and week-ahead electric load forecasting in two scenarios with different number of houses. In [38] a clustering based on AMI data is performed among customers to identify groups with similar load patterns prior to performing load forecasting, in favor of forecasting accuracy. In [39], the authors implemented a neural network (NN)-based method for the construction of prediction intervals (PIs) to quantify potential uncertainties associated with forecasts. A newly introduced method, called lower upper bound estimation (LUBE), is applied and extended to develop PIs using NN models. In [40] a new hybrid model is proposed. This model is a combination of the manifold learning Principal Components (PC) technique and the traditional multiple regression (PC-regression), for short and medium-term forecasting of daily, aggregated, day-ahead, electricity system-wide load in the Greek Electricity Market for the period 2004-2014. PC-regression is compared with a number of classical statistical approaches as well as with the more sophisticated artificial intelligence models, ANN and SVM. The authors have concluded that the forecasts of the developed hybrid model outperforms the ones generated by the other models, with the SARIMAX model being the next best performing approach, giving comparable result. The advantages that smart meters bring to load forecasting are two-fold. Firstly, smart meters offer the opportunity to distribution companies and electricity retailers to better understand and forecast the load of small scale consumers. Secondly, the high granularity and volume of load data provided by smart meters may improve the forecast accuracy of the models on the grounds that the training dataset will be more representative and will capture the highly volatile demand patterns that describe the load behavior of households or buildings. Therefore, the traditional techniques and methods developed for load forecasting at an aggregate level may not be well suited. Many different approaches are currently examined, all attempting to leverage the large volume of data derived from the numerous operating smart meters so as to improve the forecasting accuracy. In [41] seven existing techniques, including linear regression, ANN, SVM and their variants were examined. The case study was conducted on two datasets, one comprising of two commercial buildings and the other of three residential homes. The study demonstrated that these techniques could produce forecasts with high accuracy for the two commercial loads, but did not perform well for the residential ones. In [29] a self-recurrent wavelet neural network (SRWNN) was proposed to forecast the load for a building within a microgrid. The proposed SRWNN was shown to be more accurate than its ancestor wavelet neural network (WNN) for both building level and higher level load cases. Deep learning techniques for the household and building-level load forecasting are also employed across literature. In [42] a pooling-based deep RNN was proposed to learn spatial information shared between interconnected customers and to address the over-fitting problems.