## 3 Results
### Seasonal Performance Optimization and Analyses  
Because every building is exposed to different conditions that have a seasonal behaviour (i.e., different degrees of accuracy of the PV forecasts, different PV production and consumption patterns, different prices of electricity and CO\({}_{2}\) content), the last question that arises regarding this analysis is: how much these seasonal effects can affect either positively or negatively the performance of a SB? In order to answer this question, we take the best EMS strategy (i.e., best combination of scheduling strategy and quantile forecast) based on their performance for each service and for each season of the year. Table 5 presents the summary of these strategies. In this table, when a quantile forecast has two quantiles (e.g., AnEn\({}_{\tau=10.90}\)), it means that any quantile from AnEn\({}_{\tau=0.1}\) to AnEn\({}_{\tau=0.9}\) yields the same (optimal) results for that strategy. It is already very interesting to note how, for a given scheduling strategy, the forecasts that produce the best results can be so different between seasons. For instance, if we use the CO\({}_{2\text{min}}\) scheduling strategy, we require the AnEn\({}_{\tau=0.1}\) forecast in autumn to obtain the best results, whereas, in summer or spring, is the AnEn\({}_{\tau=0.4}\) forecast the one that yields \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**EMS** & \multicolumn{2}{c}{**EC\({}_{\text{min}}\)-AnEn\({}_{\tau=0.5}\)**} & \multicolumn{2}{c}{**CO\({}_{\text{2 min}}\)-AnEn\({}_{\tau=0.3}\)**} & \multicolumn{2}{c}{**GPP\({}_{\text{min}}\)-AnEn\({}_{\tau=0.4}\)**} & \multicolumn{2}{c}{**EC\({}_{\text{min}}\)-AnEn\({}_{\tau=0.1}\)**} \\
**Intended to:** & \multicolumn{2}{c}{**Minimize EC**} & \multicolumn{2}{c}{**Minimize CO\({}_{2}\)**} & \multicolumn{2}{c}{**Minimize GPP**} & \multicolumn{2}{c}{**Minimize EC**} \\  & \multicolumn{2}{c}{**favor GC**} & \multicolumn{2}{c}{**favor GC**} & \multicolumn{2}{c}{**favor GC**} & \multicolumn{2}{c}{**favor GC**} \\ \hline \multirow{2}{*}{Performance indicator} & \multicolumn{2}{c}{\% respect to the best:} & \multicolumn{2}{c}{\% respect to the best:} & \multicolumn{2}{c}{\% respect to the best:} & \multicolumn{2}{c}{\% respect to the best:} \\ \hline EC (\(\mathbf{\mathcal{E}}\) /kWh) & 0.165 & 0.0 & 0.173 & +4.8 & 0.177 & +7.3 & 0.297 & +80.0 \\ CO\({}_{2}\)(gCO\({}_{2}\)/kWh) & 65 & +3.3 & 63 & 0.0 & 64 & +1.9 & 105 & +66.5 \\ GPP (kW) & 30 & +57.4 & 30 & +57.4 & 15 & 0.0 & 30 & +57.4 \\ GC(\%) & 98.7 & \(-1.2\) & 99.4 & \(-0.5\) & 99.3 & \(-0.6\) & 99.9 & 0.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Impact of targeting one service during the scheduling over the non-targeted services. Quantile forecasts used. the best performance. In contrast, when using the GPP\({}_{\text{min}}\) strategy, any quantile forecast will produce the optimal performance in autumn, while is the AnEn\({}_{\tau=0.2}\) only in winter, which produces the best results. The versatility of using quantile forecasts for energy management is again highlighted here, as it permits customizing an EMS according to a particular service and season of the year, in order to obtain optimal performance. The strategies described in Table 5 are run for each season, and the results are summarized in Table 6. The values in parenthesis represent the percentage difference with respect to case when the same scheduling strategies are run while using the-reference-NWP forecasts. We observe in these results that most of the scheduling strategies that use quantile forecasts achieved a better performance than the same scheduling strategies using NWP forecasts. The exception is the GPP during spring, summer and autumn, where there is no improvement, and the carbon footprint in summer that presented a marginal increase when using quantile forecasts.