Returns the normalised feedin profile and installed capacity for
        a given region.

        Parameters
        ----------
        region : Region instance
        region.geom : shapely.geometry object
            Geo-spatial data with information for location/region-shape. The
            geometry can be a polygon/multi-polygon or a point.

        Returns
        -------
        feedin_df : pandas dataframe
            Dataframe containing the normalised feedin profile of the given
            region. Index of the dataframe is the hour of the year; columns
            are 'pv_pwr' and 'wind_pwr'.
        cap : pandas series
            Series containing the installed capacity (in W) of PV and wind
            turbines of the given region.

def aggregate_cap_val(self, conn, **kwargs):
        '''
        Returns the normalised feedin profile and installed capacity for
        a given region.

        Parameters
        ----------
        region : Region instance
        region.geom : shapely.geometry object
            Geo-spatial data with information for location/region-shape. The
            geometry can be a polygon/multi-polygon or a point.

        Returns
        -------
        feedin_df : pandas dataframe
            Dataframe containing the normalised feedin profile of the given
            region. Index of the dataframe is the hour of the year; columns
            are 'pv_pwr' and 'wind_pwr'.
        cap : pandas series
            Series containing the installed capacity (in W) of PV and wind
            turbines of the given region.
        '''
        region = kwargs['region']
        [pv_df, wind_df, cap] = self.get_timeseries(
            conn,
            geometry=region.geom,
            **kwargs)

        if kwargs.get('store', False):
            self.store_full_df(pv_df, wind_df, **kwargs)

        # Summerize the results to one column for pv and one for wind
        cap = cap.sum()
        df = pd.concat([pv_df.sum(axis=1) / cap['pv_pwr'],
            wind_df.sum(axis=1) / cap['wind_pwr']], axis=1)
        feedin_df = df.rename(columns={0: 'pv_pwr', 1: 'wind_pwr'})

        return feedin_df, cap