# Short-Term Power Forecasting Framework for Microgrids Using Combined Baseline and Regression Models
## 4 Proposed Framework
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Dataset Type** & **Dataset** & **Composition (Input Groups That Are Included) 1** \\ \hline CaIn & CaIn & DI \\ \hline Wa & Wa( \(\dots\)\({}^{2}\)) + DI & Wa (weather variables) and DI \\ \hline \multirow{3}{*}{Db} & Db1 + DI & Db1 and DI \\ \cline{2-3}  & Db2 + DI & Db2 and DI \\ \cline{2-3}  & Db3 + DI & Db3 and DI \\ \hline \multirow{3}{*}{DbWa} & Db1 Wa ( \(\dots\)\({}^{2}\)) + DI & Db1, Wa (weather variables) and DI \\ \cline{2-3}  & Db2 Wa ( \(\dots\)\({}^{2}\)) + DI & Db2, Wa (weather variables) and DI \\ \cline{2-3}  & Db3 Wa ( \(\dots\)\({}^{2}\)) + DI & Db3, Wa (weather variables) and DI \\ \hline Bl & Bl( \(\dots\)\({}^{3}\)) + DI & Bl (baseline rule and number of days) and DI \\ \hline BlWa & Bl( \(\dots\)\({}^{3}\))Wa( \(\dots\)\({}^{2}\)) + DI & Bl (baseline rule and number of days), Wa (indicating the weather variables) and DI \\ \hline CaIn + EXBL & CaIn + EXBL & DI and EXBL (baseline rule and number of days) \\ \hline \multirow{3}{*}{Wa + EXBL} & Wa( \(\dots\)\({}^{2}\)) + DI + EXBL( \(\dots\)\({}^{3}\)) & Wa (indicating the weather variables), DI and EXBL (baseline rule and number of days) \\ \cline{2-3}  & Db1 + DI + EXBL( \(\dots\)\({}^{3}\)) & Db1, DI and EXBL (baseline rule and number of days) \\ \cline{2-3} Db + EXBL & Db2 + DI + EXBL( \(\dots\)\({}^{3}\)) & Db2, DI and EXBL (baseline rule and number of days) \\ \cline{2-3}  & Db3 + DI + EXBL( \(\dots\)\({}^{3}\)) & Db3 and DI and EXBL (baseline rule and number of days) \\ \hline \multirow{3}{*}{DbWa + EXBL} & Db1 Wa ( \(\dots\)\({}^{2}\)) + DI + EXBL( \(\dots\)\({}^{3}\)) & Db1, Wa (weather variables), DI and EXBL (baseline rule and number of days) \\ \cline{2-3}  & Db2 Wa ( \(\dots\)\({}^{2}\)) + DI + EXBL( \(\dots\)\({}^{3}\)) & Db2, Wa (weather variables), DI and EXBL (baseline rule and number of days) \\ \cline{1-1} \cline{2-3}  & Db3 Wa( \(\dots\)\({}^{2}\)) + DI + EXBL( \(\dots\)\({}^{3}\)) & Db3, Wa (weather variables), DI and EXBL (baseline rule and number of days) \\ \hline Bl + EXBL & Bl( \(\dots\)\({}^{3}\)) + DI + EXBL( \(\dots\)\({}^{3}\)) & Bl (baseline rule and number of days), DI and EXBL (baseline rule and number of days) \\ \hline BlWa + EXBL & Bl( \(\dots\)\({}^{3}\))Wa( \(\dots\)\({}^{1}\)) + DI + EXBL( \(\dots\)\({}^{3}\)) & Bl (baseline rule and number of days), Wa (indicating the weather variables), DI and EXBL (baseline rule and number of days) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Types of datasets (combination of input groups). One of the steps inside the proposed framework is to select which of the provided datasets is most convenient to perform forecasting. The total number of inputs and outputs for a dataset, as previously described, depends on the aggregation level. Specifically, 15-min aggregations have 96 outputs for a whole day, while 60-min aggregations possess 24 outputs. The same is true for the other input fields that include measurements of previous days or baselines, where each input day implies the same number of inputs (96, 24, etc. according to the aggregation). Based on the reviewed state-of-the-art methods and considering the typical needs of the mentioned applications, the aggregation levels are typically 15 min, 1 h and (in some cases) 2 h. This means that the number of time intervals (also called bins, time periods, or timeslots, depending on the author) is equal to 96, 24 or 12 for a whole day, respectively. The architecture is designed to perform multistep predictions at 00:00 of each day, obtaining a prediction for each variable for the next 24 h (day-ahead) under the desired aggregation level. The general process is depicted in Figure 4. The dataset preparation block receives information regarding the selected measures and weather data. These fields of information are formatted and combined to create the previously described datasets. In the model training/execution block, the three modeling techniques (the baseline, RFR and MLPR) train models using each of the datasets.