## 3 Results
### Seasonal Performance Optimization and Analyses
Even when some of the improvements are marginal, there are some others that are significant, such as the carbon footprint in autumn, which decreased 24.1% or the GPP in winter that decreased 16.7% when using quantile forecasts instead of NWP. This supports the added value of using quantile forecasts in energy management to obtain better performance by customizing the EMS strategy to different operating conditions and requirements. Regarding the seasonality differences, we see clearly that, with the exception of grid-commitment (that achieved a 100% in all seasons), for the rest of services (i.e., EC, CO\({}_{2}\) and GPP), the performance is always better in summer and spring and the worst during winter and autumn. It must be recalled that several factors play a role in the differences in performance for the different services. Common to all of them, there is the annual variations in the consumption patterns. There is also the annual variability in PV output power (i.e., PV power availability), where the intensity in the PV power available as well as the longer duration of the night during winter months, is a factor that clearly impacts the performance of a system powered by photovoltaic panels. As mentioned previously, the accuracy of the forecasts also changes throughout the year. Besides the above mentioned aspects (common to all scheduling strategies), EC\({}_{\text{min}}\) and CO\({}_{2\text{min}}\) are affected by the variations of the prices and CO\({}_{2}\) content of the electricity coming from the grid. The sum of the effects of all these aspects conditions the response of the different scheduling strategies that try to find a way to achieve their goals under these constraints. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Performance Indicator** & **Winter** & **Spring** & **Summer** & **Autumnn** \\ \hline EC (\(\in\) /kWh) & 0.236 (\(-1.7\%\)) & 0.114 (\(-2.6\%\)) & 0.091 (\(-5.2\%\)) & 0.173 (\(-2.2\%\)) \\ CO\({}_{2}\) (gCO\({}_{2}\)/kWh) & 58 (\(-2.1\%\)) & 53 (\(-0.6\%\)) & 56 (\(+1.1\%\)) & 89 (\(-24.1\%\)) \\ GPP (kW) & 15 (\(-16.7\%\)) & 12 (\(0\%\)) & 9 (\(0\%\)) & 15 (\(0\%\)) \\ GC (\%) & 100 (\(+0.4\%\)) & 100 (\(+1.8\%\)) & 100 (\(+1.6\%\)) & 100 (\(+0.1\%\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Seasonal performance using the strategies of Table 5. Values in parenthesis represent performance with respect to the performance obtained when using numerical weather prediction forecasts (NWP). \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Performance Indicator** & **Best Winter EMS** & **Best Spring EMS** & **Best Summer EMS** & **Best Autumn EMS** \\ \hline EC (\(\in\) /kWh) & EC\({}_{\text{min}}\)-AnEn\({}_{\tau=0.6}\) & EC\({}_{\text{min}}\)-AnEn\({}_{\tau=0.5}\) & EC\({}_{\text{min}}\)-AnEn\({}_{\tau=0.50}\) & EC\({}_{\text{min}}\)-AnEn\({}_{\tau=0.50:00}\) \\ CO\({}_{2}\) (gCO\({}_{2}\)/kWh) & CO\({}_{\text{min}}\)-AnEn\({}_{\tau=0.3}\) & CO\({}_{\text{min}}\)-AnEn\({}_{\tau=0.4}\) & CO\({}_{\text{min}}\)-AnEn\({}_{\tau=0.4}\) & CO\({}_{\text{min}}\)-AnEn\({}_{\tau=0.1}\) \\ GPP (kW) & GPP\({}_{\text{min}}\)-AnEn\({}_{\tau=0.2}\) & GPP\({}_{\text{min}}\)-AnEn\({}_{\tau=0.20:3}\) & GPP\({}_{\text{min}}\)-AnEn\({}_{\tau=0.10:8}\) & GPP\({}_{\text{min}}\)-AnEn\({}_{\tau=0.10:9}\) \\ GC (\%) & EC\({}_{\text{min}}\)-AnEn\({}_{\tau=0.2}\) & EC\({}_{\text{min}}\)-AnEn\({}_{\tau=0.10:2}\) & EC\({}_{\text{min}}\)-AnEn\({}_{\tau=0.1}\) & EC\({}_{\text{min}}\)-AnEn\({}_{\tau=0.1}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Best combinations of scheduling strategy and forecasting method for the different seasons and services. A third question that arises after the results presented so far in this section is whether adopting a seasonal EMS strategy (i.e., using the strategies of Table 5 during the corresponding season) produces better results, by the end of the test period, than using a unique EMS strategy for each service (i.e., EMS strategies of Table 4) during the entire test period.