# Short-Term Power Forecasting Framework for Microgrids Using Combined Baseline and Regression Models
## 6 Results  
This section presents the results of applying the methodology described in Section 3 to the case study of the Savona Campus, which includes five power variables to be predicted. The two aggregation levels considered are 15 min and 1 h, and the three described forecasting techniques were tested for both configurations. The data of consumption (or generation) of each of the variables was monitored with a resolution of 1 min. For adapting these variables to 15-min and 1-h intervals, the power measurements of the interval are averaged. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Value**} & **Temperature** & **Humidity (\%)** & **Global Radiation** & \multirow{2}{*}{**Rainfall (mm)**} \\  & **(\({}^{\circ}\)C)** & & **(W/m\({}^{2}\))** & & \\ \hline mean & 15.8 & 68.5 & 174.3 & 0.36 \\ std & 7.6 & 16.6 & 251.7 & 1.92 \\ min & \(-3.9\) & 18.8 & 0.0 & 0.0 \\
25\% & 9.7 & 54.7 & 0.0 & 0.0 \\
50\% & 15.3 & 70.9 & 1.8 & 0.0 \\
75\% & 21.3 & 82.6 & 303.0 & 0.0 \\ max & 34.7 & 100.0 & 932.6 & 42.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Summary of historical weather prediction data. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Value**} & **\(ge1\)** & **\(ge2\)** & **\(ut\)** & \(lc\)** & \(pv\) \\ \hline mean & \(-2.81\times 10^{1}\) & \(9.97\times 10^{1}\) & \(2.61\times 10^{1}\) & \(1.33\times 10^{2}\) & \(1.10\times 10^{1}\) \\ std & \(4.68\times 10^{1}\) & \(5.54\times 10^{1}\) & \(4.01\times 10^{1}\) & \(6.32\times 10^{1}\) & \(1.80\times 10^{1}\) \\ min & \(-2.63\times 10^{2}\) & \(-9.43\times 10^{1}\) & \(-2.91\times 10^{1}\) & \(-1.51\times 10^{1}\) & \(0.00\times 10^{0}\) \\
25\% & \(-6.54\times 10^{1}\) & \(7.17\times 10^{1}\) & \(-2.54\times 10^{-1}\) & \(8.81\times 10^{1}\) & \(0.00\times 10^{0}\) \\
50\% & \(-3.36\times 10^{0}\) & \(9.37\times 10^{1}\) & \(-2.32\times 10^{-1}\) & \(1.08\times 10^{2}\) & \(0.00\times 10^{0}\) \\
75\% & \(9.66\times 10^{0}\) & \(1.25\times 10^{2}\) & \(5.76\times 10^{1}\) & \(1.63\times 10^{2}\) & \(1.60\times 10^{1}\) \\ max & \(5.93\times 10^{1}\) & \(4.38\times 10^{2}\) & \(1.36\times 10^{2}\) & \(4.75\times 10^{2}\) & \(8.04\times 10^{1}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of historical power data (2016–2018). The weather forecast (which have a resolution of 30 min) was not modified for use in the 15-min and 1-h aggregation models. For both types of aggregations, the weather data were directly used as inputs for the models in a row of 48 points for each weather variable (temperature, humidity, irradiation and rain), with no averaging being applied to these numbers. In the description of dataset types, one of the fields is the EXBL. It consists of the use of the baseline of a different variable as input information. Therefore, which variables are supposed to be corelated must be established to implement this type of model. Taking the fact that the EMS of the campus considers the expected load consumption to control the power resources into account, it could be useful to use \(lc\) as an external baseline for the rest of the variables. This method is discarded for \(pv\), as its behavior depends on the solar source, not on the EMS. Therefore, the three variables for which the EXBL datasets (made from \(lc\)) that were tested were \(gel\), \(ge2\) and \(ut\). The results of the experiments are presented in independent subsections for each type of technique (baseline rules and machine learning techniques, including the RFR and MLPR). Finally, a comparison of all the results is performed to check which of the models are best among all the presented techniques. It will be appreciated in these results that the obtained \(CV(RMSE)\) for the variables \(gel\) and \(ut\) are considerably larger than in the variables \(ge2\), \(lc\) and \(pv\). The reason for this fact resides in how the \(CV(RMSE)\) indicator is calculated, which was specified in Section 3.4.