Photovoltaic panels are becoming increasingly popular, and finding a suitable location for them quickly and automatically is a current and practical problem. In our experiment, we test whether a point cloud from dense multi-image matching can be useful for the automatic detection of the best locations for installing photovoltaic panels. We propose a methodology for processing and analyzing UAV point clouds, where the use of deep learning in combination with the CANUPO algorithm results in high roof recognition efficiency. Two classes were selected: roofs and non-roof objects. This made it possible to filter the detected roofs and remove erroneous objects. The resulting model detected buildings with an accuracy of approximately 80% and an effectiveness of 100% (there were no false detections). the following factors were taken into account in the insolation calculations: roof angles, roof slope exposure, changes in the angle of sunlight throughout the year, and atmospheric transmittance. The roof angles and exposure were determined using a Digital Surface Model (DSM) generated from multi-image UAV data. In our research, we took into account the average angle of incidence of sunlight throughout the year and at quarterly intervals. The use of DSM for roofs and the SVC algorithm combined with CANUPO made it possible to eliminate false detections and significantly increase the effectiveness of location detection. Research conducted for the entire year and quarters enabled the analysis of changes in roof insolation throughout the year, which is crucial when estimating the profitability of installing photovoltaic panels.