# Short-Term Power Forecasting Framework for Microgrids Using Combined Baseline and Regression Models
## 2 State-of-the-Art Methods  
In the field of power forecasting, a wide variety of techniques and methods have been proposed in the literature, and their ambit of application depends on the characteristics of the forecast, such as the time aggregation and horizon of prediction. The two main types of forecasting are deterministic and probabilistic methods. Deterministic forecasting results in point outputs, with one value at each step, while probabilistic forecasting assigns probabilities to the various scenarios; these can be in the form of quantiles, intervals, or density functions [30]. Both approaches are used in related existing methods to obtain probabilistic forecasts from deterministic methods, such as bootstrapped prediction intervals (BPIs) and quantile regression averaging (QRA) [31]. Regarding deterministic methods, an extensive summary of the different forecasting approaches can be found in [32], where the authors review different electrical load forecasting methods and analyze their convenience according to the time aggregation, the time required for forecasting and the most convenient type of data used as input. In particular, the analysis covers 113 different case studies reported across 41 academic papers. The different methods are categorized as \"regression\" methods, \"bottom up\" approaches, \"artificial neural networks\" (ANNs), \"support vector machines\", \"time series analysis\" methods, and other techniques used in mid-term forecasting. Additionally, several tables and graphs are provided to show the situations in which these methods are most popular. Reference [30] contains a review of the applications of probabilistic methods in load forecasting. Despite these methods being of great interest, they present some additional problems from the point of view of their evaluation processes, as the typical metrics used for deterministic methods are not valid due to the existence of quantiles, prediction intervals or confidence intervals. This is why some authors have proposed metrics such as the pinball score or the Winkler score [30; 33]. Due to these additional difficulties and considering that many energy management applications are based on deterministic forecasts (for example, the abovementioned approaches [22; 23; 24]), the present paper is focused on deterministic methods, with the inclusion of probabilistic techniques being a future research topic. The types of techniques that can be found in the literature are dependent on the ambit of application, such as the customer level, building level, renewable generation units, microgrids of diverse sizes, distribution networks, or even market level (e.g., price forecasting [31]). In [34], [PERSON] and [PERSON] review the use of ANNs for forecasting energy use in buildings. A list of found methods that select hyperparameters is also detailed, including (i) heuristics (such as rules of thumb), (ii) cascade correlation, (iii) evolutionary algorithms (EA) and (iv) automated architectural search. The comparison of reviewed papers includes information about the time steps, forecast horizons, and error metrics used by these methods. Other details about the limitations of ANN-based forecasting are explained. As previously mentioned, the influence of weather conditions is of high importance due to the increasing use of renewable generation, such as solar and wind plants, which present power generation effects that change with atmospheric conditions. For this reason, many authors have studied how to consider these conditions to improve forecasting methods. In this sense, reference [35] contains recommendations for the use of weather data in microgrid-related forecasts. The authors especially recommend using real forecast data (received from a public or commercial forecasting service) for modeling, not any other synthetic dataset (i.e., data specifically designed for the given experiment with different degrees of similarity relative to reality). The importance of clearly defining the error metrics to be applied is also pointed out.