# Novel Data-Driven Models Applied to Short-Term Electric Load Forecasting
## 2 Related Works  
There is a large body of recent work on STLF applying many different techniques which show that even when it may be seen as a well-known area of study, it is still an important area of research due to its technical difficulty and economic impact of having an accurate load forecast. The intention of this work is to contribute to this area by providing practical results on the application of some of the newest methods for time-series forecasting applied to STFL in an extensive and homogeneous way. We will present related works considering the applied methods, global review studies, and some of the anticipated applications that result from having accurate forecasts. The presentation will focus more on adopted methods and processes than on performance metric comparison, since the diversity of datasets, the difference in load magnitudes, the differences in the implementation of the metrics, and the various test/validation procedures make it very difficult to perform a homogeneous comparison of results. **Review of current techniques:** The work in [2] presents a comprehensive review of the techniques used to forecast electricity demand, analyzing the different types of forecasts, parameters affected, and techniques used, together with a literature review and a taxonomy of the main variables involved in the problem. The work in [13] presents a detailed review of recent literature and techniques applied for building energy consumption modeling and forecasting. A complete review of machine learning for load prediction is given in [31] with emphasis on the following methods: tree-based methods and random forest, support vector regression (SVR), neural networks, ensemble learning, statistical methods (ARMA, ARIMA,...), gaussian processes, k-nearest neighbors regressor (KNN), and fuzzy time-series algorithms. An alternative review is given in [32] highlighting the following models: linear regression, artificial neural networks (ANN), SVR, statistical methods, restricted Boltzmann machine, and deep learning (CNN and LSTM). A recent review of machine learning models applied to load forecasting focusing on performance metrics results is proposed in [33]. This study presented the following methods as the most frequently implemented in electric power forecasting: statistical methods, artificial neural networks (ANNs), support vector machines (SVMs), decision trees (DTs), adaptive neuro fuzzy inference systems (ANFISs), and recurrent neural networks (RNNs). The work in [34] presents the application of five machine learning models for STLF: multiple linear regression, KNN, SVR, and gradient boosting. The best performance is obtained for gradient boosting. Authors in [35] present a recent and systematic review of techniques applied to load forecasting with an emphasis on neural networks and hybrid models. The main models presented are: SVM, GBM, random forest (RF), KNN, neural networks, decision trees, statistical methods, CNN, LSTM, and the adaptive neuro-fuzzy inference system (ANFIS). The authors in [36; 37] provide an study of methods to use in load forecasting considering different forecasting problems and objectives. The main methods discussed are: statistical methods, AdaBoost, GBM, random forest, generalized additive models (GAM), gaussian processes, KNN, linear regression, and support vector regression. The work in [38] presents an extensive literature review with no mention of dynamical systems modeling or deep learning or ensemble deep learning models. The methods mentioned are: multioutput linear regression, generalized additive models, statistical models, neural networks, support vector machines, gradient boosting, and fuzzy regression. The review in [39] favors the use of artificial neural networks, support vector regression, and fuzzy logic models. **Dynamical systems modeling:** There is a growing current interest in the application of dynamical systems analysis tools based on reduced-order models and, in particular, in the use of dynamic mode decomposition to STLF.