# Novel Data-Driven Models Applied to Short-Term Electric Load Forecasting
## 2 Related Works
The work in [5] provides a DMD study applied to electric load data from a utility operator in Queensland, Australia. In this work, the DMD algorithm presents better forecasting results than classic time-series autoregressive approaches. They offer a one day ahead forecast based on the load values of the previous 4 days, presenting a MAPE result of 2.13. A similar application of DMD is carried out in [40] but applying DMD to predict forecast errors followed by an extreme value constraint method to further correct the forecasts. The algorithm is applied to actual load demand data from the grid in Tianjin, China, and the results obtained with DMD are compared with a series of additional techniques (autoregressive moving average, neural networks, support vector machines, extreme learning machines, etc.). According to the authors, the proposed method shows greater accuracy and stability than alternative ones, with a best average root mean squared error (RMSE) of 476.17. In [41], the authors employ an empirical mode decomposition technique to extract different modes from the load signal, and apply an independent deep belief network for each mode prediction, with a subsequent aggregation of results (ensemble) to obtain the final load forecast. More classic ensemble techniques for forecasting electricity consumption in office buildings are investigated in [42], comparing gradient tree boosting (GTB), random forests (RF), and a specifically adapted AdaBoost model that presents the best results. **Classic machine learning:** A substantial number of works have presented several classic machine learning models to STLF. A feed-forward artificial neural network (FF-ANN) is used in [43] to forecast the electricity consumption for residential buildings for 24 h. The results are compared with other models including GTB and RF, selecting the best model at each forecast iteration. The best average result for the different test iterations is obtained for the ANN with an RMSE of 2.48. The work in [44] presented a theoretical review of the most commonly used ML methods for STLF, including ANN and a support vector for regression. The work in [45] presents an interesting pipeline method based on combining a feature transformation using a clustering approach followed by a support vector regression. The authors in [46] introduce an additive regression model for STLF. **Statistical analysis methods:** Time-series statistical analysis models for STLF are discussed in detail in [1] with forecasts at an hourly interval applied to load data from the Electric Reliability Council of Texas (ERCOT). They present results applying ARIMA and Seasonal Autoregressive Integrated Moving Average (SARIMA) models achieving an average mean absolute percentage error (MAPE) between 4.36% to 12.41%. **Sequence to sequence models:** The sequence to sequence (Seq2 seq) architecture that originated in the field of natural language processing (NLP) has been applied in recent works to STLF. The authors in [47] apply different Seq2 seq architectures, comparing them with other DL models based on recurrent and convolutional layers. The models are applied to two different datasets (scenarios): one for an individual household electric power consumption data set (IHEPC) located in Sceaux, France, and the other for the GEFCom2014 public dataset made available for the global energy forecasting competition 2014. The best results (RMSE between 17.2 and 0.75 depending on the scenario) are obtained with convolutional and recurrent architectures together with deep neural networks with dense layers. Considering average results, the Seq2 seq models do not provide the best results. The conclusions obtained in this work are consistent with the results obtained by the present study. A similar study is presented in [48], where research was conducted comparing a Seq2 seq model (with and without attention) with alternative DL models based exclusively on different types of recurrent networks, such as long short-term memory network (LSTM) and gated recurrent unit (GRU).