# Novel Data-Driven Models Applied to Short-Term Electric Load Forecasting
###### Abstract  
This work brings together and applies a large representation of the most novel forecasting techniques, with origins and applications in other fields, to the short-term electric load forecasting problem. We present a comparison study between different classic machine learning and deep learning techniques and recent methods for data-driven analysis of dynamical models (dynamic mode decomposition) and deep learning ensemble models applied to short-term load forecasting. This work explores the influence of critical parameters when performing time-series forecasting, such as rolling window length, k-step ahead forecast length, and number/nature of features used to characterize the information used as predictors. The deep learning architectures considered include 1D/2D convolutional and recurrent neural networks and their combination, Seq2 seq with and without attention mechanisms, and recent ensemble models based on gradient boosting principles. Three groups of models stand out from the rest according to the forecast scenario: (a) deep learning ensemble models for average results, (b) simple linear regression and Seq2 seq models for very short-term forecasts, and (c) combinations of convolutional/recurrent models and deep learning ensemble models for longer-term forecasts. short-term electric load forecasting; deep learning; machine learning; dynamic mode decomposition; deep learning ensemble model +
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attention [8; 9; 10], and (d) deep learning ensemble models specially targeted for time-series forecasting [11]; and a set of well-known forecasting models, such as: (e) classic machine learning (ML) models: linear regression, random forest, gradient boosting, k-nearest neighbors, support vector regression and AdaBoost [12; 13; 14; 15], (f) multi-layer perceptron [2], and (g) deep learning models based on separate CNN and RNN networks [13; 14]. By jointly evaluating the novel or less used techniques with the best known, we can compare their performance in different scenarios. We carried out the study by applying the different models to a real dataset of power consumption from a Spanish utility for the province capital of Soria (Spain). This dataset has been extensively studied previously [16; 17; 18; 19; 20; 21; 22; 23]. This work is different from other studies: (a) by applicability, not being a reference to other studies and applying all the models to the same dataset which allows comparison of results, (b) by novelty, by including, in the same study, the results from DMD [5], new DL ensemble models based on gradient boosting principles [11], specific configurations of CNN/RNN [6; 7], and Seq2 seq models [8; 9; 10], and (c) by extension, by considering a large number of different models under different scenarios. STLF needs a previous preparation of the load values used as predictors. The preparation consists of aggregating these values in discrete time intervals (time-slots) that can be seconds, minutes, or hours, and the forecast can be based on a different number of previous values (predictors) that are obtained with a rolling window process applied to the past values. The length of the rolling window defines the number of predictors. With these predictors, the forecast can be extended to the following time-slot or to several time-ahead time-slots (k-step ahead forecast). Finally, the value of the predictors can be a scalar (load value), or a vector composed of the load value plus additional information, such as date/time or weather data. Considering the scenario with vector predictors and multiple time-ahead forecasts, we arrive at a challenging multivariate multi-output regression problem. The influence of these parameters: (a) length of rolling window, (b) length of time-ahead forecast, and (c) features used to form the predictors, is analyzed in this research in combination with the different natures exposed by the models.