Solar photovoltaics (PV) is currently the fastest growing type of electrical energy generation. A substantial share is distributed, and key information - such as their installed capacity, precise location, tilt, and azimuth - are often lacking or inaccurate. Therefore, obtaining accurate data on existing PV systems become increasingly critical to determine optimal locations for adding new PV capacity, in terms of ensuring grid stability. Recent advances in identifying and segmenting solar energy systems, using aerial imagery, point to a logical next step; enhancing the modelling of tilts and azimuths, as these influence the power output significantly. Therefore, a method is proposed that derives the tilt and azimuth of solar energy systems using Light Detection and Ranging (LiDAR) data. Polygons representing solar energy systems, identified in aerial images, are orthorectified to LiDAR data and then linear regression is applied to determine the orientation. The method is evaluated for 3'500 Swedish solar energy systems previously identified in aerial images, with a manually derived ground truth azimuth dataset. For 91%-95% of the systems, the model accurately estimated the azimuth within a margin of 3 degrees. Furthermore, the distribution of azimuths was more narrow for solar thermal systems than for PV systems. Aground truth of the tilt fora subset of 39 systems gave a mean absolute error of 3.6 degrees. The proposed method is believed to provide more accurate PV metadata to, e.g., aggregators and grid operators, enabling more precise PV power simulations and forecasts, in turn leading to better grid operation and planning.