# Short-Term Power Forecasting Framework for Microgrids Using Combined Baseline and Regression Models
## 4 Proposed Framework  
In the field of power forecasting, it is common to use datasets that, apart from power values (with their corresponding timestamps), also include weather information such as temperature and humidity data. Moreover, this information can (and should) be enriched with the inclusion of extra information. This additional information could be date-related information (e.g., days of the week, workdays) or the inclusion of previous measurements (e.g., the raw measurements taken on some previous days). Furthermore, as part of the proposals of the present paper, it is possible to include not only the raw data of previous days (which is usually done by other authors) but also their corresponding baseline forecasts obtained with the previously described rule-based baseline method. Specifically, up to five groups of possible inputs (described in Table 1) are proposed for this framework. The exact number of inputs in some of these groups depends directly on the aggregation of the forecasted variable (the number of time intervals considered in a whole day) and the aggregation of weather forecasts (which could be different from the aggregation of the forecasted variable). \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Input Group** & & **Description** \\
**Names** & year month & Year Month \\ DI (stands for day information) & dayM dayW weekend & Day of the month Day of the week Whether the day is a weekend day (Saturday or Sunday) \\  & holiday sw & Whether the day is a holiday Daylight saving time (summer or winter). \\ \hline Wa(thsr) & Temperature (t), humidity (h), irradiation (s) and rain (r). & Day-ahead weather forecast for the whole day. The number of time intervals could be different from the number of intervals of the variable. \\ Wa(ths) & Temperature (t), humidity (h), irradiation (s) & Day-ahead weather forecast for the whole day. The number of time intervals could be different from the number of intervals of the variable. \\ \hline Db1 & Measurements of day \(-1\) & Power values for all time intervals of the previous day. \\ Db2 & Measurements of days \(-1\) and \(-2\) & Power values for all time intervals of the two previous days. \\ Db3 & Measurements of days \(-1\), \(-2\) and \(-3\) & Power values for all time intervals of the three previous days. \\ \hline Bl(sn) & & The baseline1 forecast of the variable to be predicted using a certain rule, with \(n\) as the chosen hyperparameter. \\ Bl(wsn) & Day-ahead forecast using rule-based baseline models & The baseline1 forecast of the variable to be predicted using a certain rule, with \(n\) as the chosen hyperparameter. \\ Bl(chwn) & & Baseline1 forecasts of a variable that are different from the variable to be predicted, with \(n\) as the chosen hyperparameter. As this baseline is from a different variable, it is called an “external baseline” (EXBL). \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Types of inputs. Day information includes representative data about the date, such as the year, the month, and the day of the week. The external baseline (EXBL) consists of a variable baseline that is different from the objective to be predicted (i.e., not the target variable but another variable of the microgrid, such as the load consumption). The reason for this inclusion is to take advantage of the correlations between the different variables (i.e., load consumption and the variable to be predicted) of the microgrid to improve the forecasting results. The datasets that include an EXBL are identified on their tags at the end of their names. From the combinations of all these fields, multiple datasets can be generated to train models. In this sense, Table 2 summarizes the different datasets proposed for this forecasting framework. To simplify the identification of the datasets, a tag (\"dataset type\") is assigned to those similar datasets. Each tag summarizes the information included in the datasets (calendar information, weather, previous measurements, etc.) using a simple, short name.