# Short-Term Power Forecasting Framework for Microgrids Using Combined Baseline and Regression Models
## 2 State-of-the-Art Methods
Regarding wind power generation, some approaches followed in existing studies for short-term and ultrashort-term forecasting include the use of autoencoders and back propagation [36], numerical weather prediction [37], and long short-term memory (LSTM) [38; 39]. Thus, other approaches apply wavelet decomposition combined with machine learning techniques [40]. In the field of photovoltaic generation, one study [33] presents a method for probabilistic forecasting based on LSTM and quantile regression averaging (QRA). A 24-h-ahead forecasting method based on synthetic weather forecasting and LSTM is applied in [41]. In applications that are more focused on small customer forecasting, which mainly includes load consumption, customer segmentation by type is a very common approach. In [42], customers were divided into clusters to model and forecast the next 12 h of consumption. The existence of photovoltaic generation on the customer side and its effect on total consumption are also considered in [43]. In [44], a comparison of techniques was performed over a single household using one month of data, showing the superiority of machine learning techniques over linear regression. The inclusion of appliance (dishwashers, televisions, etc.) measurements have been shown to be useful for discovering resident behaviors and improving forecasting results, as described in [45]. Some examples of studies including buildings with greater consumption (such as universities) can also be found in the literature. A study of power consumption forecasting on a university campus is presented in [46] using conditional linear predictions. The authors compare the performance of a method using between 1 and 96 bins (the number of divisions in which a day is divided for forecasting), showing how an 8-bin division achieves more accurate performance than the use of 96 bins. In [47], [PERSON] et al. present a prediction model for campus buildings based on occupancy patterns. This is a good example of how knowledge of building use can be of great interest for consumption prediction. Finally, regarding applications at the distribution network level, ANNs are applied in [48] to predict the maximum demand response over a secondary distribution network in India. The aim is to avoid violations of the permitted contractual demand limit by utilities. The study of consumption peaks is of particular interest for congestion management applications. Due to their importance and their difference from other more general forecasts, some authors have proposed specific methods and evaluation metrics for peak predictions, as in [49], where the proposed metric reduces penalization in cases of peak deviations between the real data and the forecasts. Many of these papers propose the use of artificial intelligence techniques, such as those used in the present paper, to perform forecasting. This type of method has been shown to obtain better results than other classical methods (such as statistical methods) in many situations. However, it is worth mentioning that in some cases, artificial intelligence methods constitute black-box models, so their behaviors sometimes cannot be easily explained. In this regard, some authors have proposed the use of rule-based methods that introduce expertise-based knowledge to permit the extrapolation of models in some cases [50] or that substitute black-box models by others that are more understandable [51]. Another type of forecasting model includes baselines, which are frequently obtained from the measurements taken on some previous similar days [30; 49] or from similar groups of customers [52]. In the context of new flexible service applications, baseline models could be considered good approaches for obtaining the expected consumption levels of customers, making it possible to evaluate their availability and audit their performance in flexible service scenarios.