# Short Term Electric Load Forecasting Based on Data Transformation and Statistical Machine Learning
## 1 Introduction
Load forecasting at this level is very mature research-wise and there is a great volume of literature describing and testing a number of methods and algorithms, including ANNs, Support Vector Machine (SVM), ARIMA, exponential smoothing, fuzzy systems, and linear regression. Recent methods upon load forecasting can be found in [25]. However, the loads of a house, a factory, and a feeder are more volatile than HV level loads. Therefore, developing a highly accurate forecast at lower consumption levels is nontrivial. Although the majority of the load forecasting literature has been dedicated to forecasting at the top (high voltage) level, the information from medium/low voltage levels offers a promising field of research. The highly volatile nature of house load time series makes it particularly difficult to achieve very accurate predictions. In [26] the authors showed that the \"double peak\" error for spiky data sets means that it is difficult to measure the accuracy of household-level point forecasts objectively introducing traditional pointwise errors. Similar methods have been applied at the household level as at the HV level, including ANNs [27; 28] ARIMAs, wavelets [29], Kalman filters [30] and Holt-Winters exponential smoothing [31]. The prediction errors of these methods are much higher compared to those reported at the HV level, with MAPEs ranging from 7% up to 85% in some cases. Regardless the voltage level and the load scale, load forecasting problems share certain common factors that may affect the prediction accuracy of energy consumption. These are the energy markets, the variables affected by the weather and the hierarchical topology of the grid. In competitive energy retail markets, the electricity consumption is largely driven by the number of customers. Since the number of customers is uncertain, the load profile is consequently characterized by high stochasticity. In [32] the authors propose a two-stage long-term retail load forecasting method to take customer reduction into consideration. The first stage forecasts each customer's load using multiple linear regression with a variable selection method. The second stage forecasts customer attrition using survival analysis. Then, the final forecast results from the product of the two forecasts. Another issue regarding the energy market is the demand response programs which pose another challenge in load forecasting, since some consumers are willing to alter their consumption patterns according to price signals, while others are not. In [33]the authors detect the price-driven customers by applying a non-parametric test so that they can be forecasted separately. Across literature, many papers have shown strong correlations between weather effects and demand. A weather variable investigated in literature is humidity [34], where the authors discovered that the temperature-humidity index (THI) may not be optimal for load forecasting models. Instead, more accurate load forecasts than the THI-based models were performed when the authors separated relative humidity, temperature and their higher order terms and interactions in the model, with the corresponding parameters being estimated by the training data. In [35] the load of a root node of any sub-tree was forecasted first. The child nodes were then treated separately based on their similarities. The forecast of a \"regular\" node was proportional to that of the parent node, while the \"irregular\" nodes were forecasted individually using neural networks. In [36] the authors exploit the hierarchical structure of electricity grid for load forecasting. Two case studies were investigated, one based on New York City and its substations, and the other one based on Pennsylvania-New Jersey-Maryland (PJM) and its substations. The authors demonstrated the effectiveness of aggregation in improving the higher level load forecast accuracy.