# Short-Term Power Forecasting Framework for Microgrids Using Combined Baseline and Regression Models
## 6 Results
### Comparison and Discussion  
Once the results are obtained, the different models for each objective variable can be compared. This comparison is reported in Table 5, which gives the \(CV(RMSE)\) value for the best model according to each variable, aggregation, and dataset type. To simplify the visualization of the results, the category \"XX+EXBL\" (the datasets that include external baseline information, as shown in Table 5) is not detailed for each type of baseline rule, and only the model with the best indicator value is kept. Some reference models are also included, such as _baseline_s1_ (previous day) and _baseline_s7_ (mean of the seven previous days). The _baseline_s1_ method is finally the one chosen as a reference method, as here it is considered analogous to the naive method. Moreover, the percentage of improvement (13) is calculated in a way similar to that reported in [36] (with _baseline_s1_ as the reference method), and the \(CV(RMSE)\) for the reference model and the proposed model are shown. \[I_{improvement}=\frac{e_{reference\ model}-e_{proposed\ model}}{e_{reference\ model}}\cdot 100\% \tag{13}\]  
These results can be seen in Table 6. The \(CV(RMSE)\), \(RMSE\) and \(nRMSE\) indicators for the best models are given. Specifically, the \(nRMSE\) values are calculated considering the magnitudes that are included in Figure 5.  
As previously indicated, the table of improvement (Table 6) uses _baseline_s1_ as reference. However, it can be appreciated that this table is directly obtained from the numbers in Table 5 of the results, so it is possible to calculate the relative improvements considering any other model from those included in the table applying expression (13). Table 5 shows some interesting data about the behaviors of the evaluated models and input dataset types. First, it can be observed that the models obtained using the RFR and MLPR are better than the best baseline models. This is expected, as according to the reviewed literature, these machine learning models usually achieve good results in the current field of study. However, it should not be forgotten that baseline models are still useful in some scenarios. They are very easy to calculate, give information about tendencies and are very clear in their behavior. Moreover, they are suitable for application in the auditory process in flexibility agreements, as a customer could be more disposed to accept one of these baseline rules than trained models, as they are RFRs and MLPRs. Second, it is interesting to note that the use of sets that include external baselines (EXBLs) yield the best results for \(ge1\), \(ge2\) and \(ut\). This fact can be explained considering that in the campus (\(ge2\)), the zone under the direct control of the EMS is precisely the microgrid (\(ge1\)) where the microturbines (\(ut\)) are installed. It is therefore logical that \(ge1\), \(ge2\) and \(ut\) achieve good performance when they are modeled considering the load of the campus (\(lc\)), as the EMS estimates it to manage the microgrid. Regarding the forecasting of the load of the campus (\(lc\) variable), the datasets that include information from previous days and weather predictions for these days (dataset types \"DbWa\" and \"BIWa\") yield the best results under the MLPR. In the case of photovoltaic generation (\(pv\) variable), the results show that the inclusion of previous day measurements together with weather information (dataset types \"DbWa\" and \"BIWa\") improves the modeling results. The RFR outperforms the MLPR for this variable. The proposed models achieve accuracy improvements of up to 62% and a mean of 37% (average improvement value across the five variables and the two aggregations evaluated) over that of the reference model, which is _baseline_s1_ (see Table 6). Finally, as an example, some illustrative comparisons between the actual data, the best baseline and the best model are shown in Figure 8 for each of the five variables. The \(CV(RMSE)\) for each of the individual days is calculated for both the baseline and the best model.